2020 Handbook on AI and International Law by Indian Society of Artificial Intelligence & Law

Æ 2020 HANDBOOK ON AI AND INTERNATIONAL LAW First Edition (2021)

By the Indian Society of Artificial Intelligence and Law

International Law

2020 Handbook on Artificial Intelligence and International Law

Year: 2021 Date of Publication: February 12, 2021 Editors: Abhivardhan, Suman Kalani, Akash Manwani & Kshitij Naik. ISBN (online): 978-81-947131-7-3 ISBN (paperback): 979-86-626872-2-6 All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the publisher and the authors of the respective manuscripts published as papers, except in the case of brief quotations embodied in critical reviews and certain other noncommercial uses permitted by copyright law. For permission requests, write to the publisher, addressed “Attention: Permissions Coordinator,” at the address below. Printed and distributed online by Indian Society of Artificial Intelligence and Law in the Republic of India. First edition, 2020 Handbook on AI and International Law, 2021. Price (Online): 250 INR Price (Paperback): 20 USD (Amazon.com) Indian Society of Artificial Intelligence and Law, 8/12, Patrika Marg, Civil Lines, Prayagraj, Uttar Pradesh, India – 211001 The publishing rights of the papers published in the book are reserved with the respective authors of the chapters and the publisher of the book. The copyright of all the papers is reserved with the authors of the respective papers. For the purpose of citation, please follow the format for the list of references as follows: 2020. 2020 Handbook on AI & International Law. Prayagraj: Indian Society of Artificial Intelligence and Law, 2020. 978-81-947131-7-3, 979-86-626872-2-6. You can also cite the book through citethisforme.com (recommended). For Online Correspondence purposes, please mail us at: editorial@isail.in For Physical Correspondence purposes, please send us letters at: 8/12, Patrika Marg, Civil Lines, Prayagraj, Uttar Pradesh, India - 211001

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Limits of Liability and Disclaimer of Warranty The information contained in this book is true to correct and the best of contributors & the publisher's knowledge. The contributors have made every effort to ensure the accuracy of these publications, but cannot be held responsible for any loss or damage arising from any information in this book. All trademarks referred to in the book are acknowledged as properties of their respective owners.

2020 Handbook on Artificial Intelligence and International Law

Preface

International Law has transformed and much transfused with other unknown fields in various sciences per se. AI Ethics is one of the emerging fields, where, policy intervention, in line with the idea of multilateralism has emerged merely recently. This emergence is not something pre-decided, but is usually gauged by some countries and some special non-state actors like the UN, for example, and non-state actors, which includes startups, NGOs and civil society actors most of the times. Works such as the Beijing Consensus on AI and Education, 2019, the 2017 Asilomar Conference on Beneficial AI, DARPA’s conception of Explainable AI & many more have endorsed a sense of research aptitude and rationalization of the field of AI Ethics in Law, Policy and International Affairs. Our team of research contributors and analysts at the Indian Society of Artificial Intelligence and Law, have therefore at our very best, prepared a Handbook, in two parts, which caters to some important and influential fields of international law, and its synergy with AI Ethics. This handbook, with utmost humility is not some research encyclopedia. It serves to ignite curiosity and make people rethink or think differently about the way we see AI in our lives. It is a researched handbook, which has been edited by Professor Suman Kalani, Chief Research Expert of ISAIL (also the Assistant Professor at the SVKM’s Pravin Gandhi College of Law, Mumbai, India), Kshitij Naik, Chief Strategy Advisor of ISAIL, Akash Manwani, Chief Innovation Officer of ISAIL and me. We have tried to give crisp and detailed case studies on various dynamic fields of AI and international governance, which consist in AI & International Affairs, AI & Society, AI & Ecology, AI & Governance & other miscellaneous chapters, such as on Emerging Technologies and Applied Sciences. When you read the book, please do not treat it as some mere answer to all of your questions. Instead, relish the ideas and realities which have been expressed in this work. The chapters reflect some generic notions of international law, which have been widely accepted worldwide, and at the same time, might be an attempt to compel the readers to maybe come up with a reasonable policy intervention per se. We hope the readers would have a suitable time reading this book per se. I would like to render a special thanks to Avani Tiwari and Aryakumari Sailendraja for their motivation throughout the completion of the first book of the project. This book (the 1st one) is a part of the Indian Strategy on AI and Law Programme, specifically in the AI Education Initiative. Abhivardhan Chairperson & Managing Trustee Indian Society of Artificial Intelligence and Law

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Foreword

This remarkable handbook is a welcome and timely contribution presented by the Indian Society of Artificial Intelligence & Law (ISAIL). It provides a wealthy of information, case studies, and prescient analyses on a number of important topics for research on the far-reaching implications of artificial intelligence (AI), which encompass from privacy, human rights, and intellectual property law to specific, concrete domains, such as energy, outer space, civil aviation, education, and health, to name just a few. Thanks to a wide-ranging choice of topics and a multidisciplinary approach, the issues examined in this volume include not only international law, but ethics, philosophy, politics, diplomacy, economics, computer engineering, and more. Such well-rounded undertaking is commensurate with the significance of the current debate on the many challenges brought about by AI systems in society, law, and our daily lives. Emerging technologies will have an increasing influence on our decisions over the years and decades ahead, which makes imperative to anticipate, identify, and mitigate any risks that can cause undesirable consequences. The more powerful the technology is, the more critical becomes to ensure that it will be safe, responsible, and beneficial to people across the globe. With the rapid development of machine learning algorithms, human judgement and skills have been replaced by automation or AI-enabled solutions in several cognitive tasks, including in the field of law. However, we need to be extremely careful before fully delegating decision-making processes to machines or AI systems. In some cases, such as in life and death decisions, moral considerations are paramount and would undoubtably suggest that the line must be drawn somewhere. AI ethics has been developing precisely to ask these difficult questions. Is it right to give machines unlimited power? Are there decisions that an AI should not be allowed to make? Should governments seek to regulate the technology before harm has been done? How can new rules, legislation frameworks, or institutions be designed to safely guarantee that the technology will be used in benefit of society as a whole, while also respecting the fundamental rights of the human person? The ultimate goal should be to maximize the well-being of all AI users and, at the same time, minimize any negative impact or unintended outcomes. Many high-level principles and governance tools have been put forward in ongoing discussions on the matter, from human-centred and trustworthy AI to voluntary codes of conduct, regimes, and recommendations on accountable research and development for states, private companies, and other key players. We have recently been witnessing a continuous and extensive search for more transparency, accountability, explanability, fairness, impartiality, non-

2020 Handbook on Artificial Intelligence and International Law

discrimination, safety, security, and reliability of AI systems. Certainly, numerous technical problems and ethical pitfalls still need to be overcome: responsibility issues under the law, biased algorithms, opacity, lack of oversight, misleading or unexplainable outputs, machine failure, malfunction, accidents, data breaches, and digital abuses of all kinds. In addition to, for instance, establishing ethical boards and complying with legislation, audits, and certification schemes, addressing these challenges in the long run will likely require setting minimum standards, norms, performance metrics, robust engineering, and safety measures. In all these discussions, the role of international law remains crucial to prevent fragmentation and promote global cooperation, shared values, and commonly accepted principles that can enhance long-term trust in the ethical use of AI technologies. It is important to note that formal international treaties are only one option to be considered. There is a wide array of soft law alternatives that can be explored in both policymaking and with regard to the international governance of AI. In doing so, it is essential that all stakeholders participate and have their say. Diversity and inclusion are sorely needed to avoid discrimination and underrepresentation, particularly in situations affecting developing countries of the Global South. If the declared objective of most AI initiatives is not to perpetuate inequalities, proper geographical and gender representation should be among the first steps to realize this vision. Intellectually stimulating and empirically rich, I hope this handbook will soon become an invaluable reference for lawyers, political scientists, experts, and all those interested in seriously investigating the ethical dilemmas and legal implications of AI at the international level. I congratulate the editors for this laudable effort and wish every success to ISAIL in its future endeavours.

Eugenio V. Garcia, PhD Diplomat, Researcher on AI Governance currently Head of Diplomatic Mission based in Guinea-Conakry January 20, 2021

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Acknowledgment

Amrit Subhadarsi Assistant Professor of Law, KIIT Law School, KIIT (Deemed) University, India Abhinav Misra Invited Fellow, Institute for Intellectual Property Law, Foundation for Intellectual Property, Tokyo Sushanth Samudrala Member, Expert Panel on Law, Policy & International Affairs, Indian Society of Artificial Intelligence & Law, India Manohar Samal Research Analyst at Internationalism C/O AbhiGlobal Legal Research & Media LLP, India

2020 Handbook on Artificial Intelligence and International Law

Table of Contents

Section 1: Basis. 1. Introduction to AI Ethics & Education Section 2: AI and International Affairs. 2. The Basics of International Law 3. International Law, Diplomacy and Dispute Settlement 4. International Law & the Role of Actors Section 3: AI and Digital Studies. 5. International Law and Technology 6. International Privacy Law 7. International Intellectual Property Law 8. International Cybersecurity Law 9. International Telecommunication Law Section 4: AI and Ecology. 10. International Environmental Law 11. International Energy Law 12. International Space Law 13. International Civil Aviation Law 14. International Law and Applied Sciences Section 5: AI and Economics. 15. International Labour Law 16. International Sea Law Section 6: AI and Governance. 17. International Humanitarian & Refugee Law 18. International Criminal Law 19. International Law and Internet Governance Section 7: AI and Society. 20. International Human Rights Law 21. International Cultural Law 22. International Health Law References.

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The Handbook Team

Editors-in-Lead. Abhivardhan Chairperson & Managing Trustee, Indian Society of Artificial Intelligence and Law Prof Suman Kalani Chief Research Expert, Indian Society of Artificial Intelligence and Law Akash Manwani Chief Innovation Officer, Indian Society of Artificial Intelligence and Law Kshitij Naik Chief Strategy Advisor, Indian Society of Artificial Intelligence and Law Contributors. Abhivardhan Abhivardhan is the Chairperson & Managing Trustee of the Indian Society of Artificial Intelligence and Law, the Chief Executive Officer, Internationalism C/O AbhiGlobal Legal Research & Media LLP and the President of Global Law Assembly. He is also a Convening Member of the Interest Group on International Law and Technology in the European Society of International Law. His areas of research include AI Ethics and Policy, International Law, Indic Constitutional Policy, Populism, Plurilateralism, Multipolarity & Indian Foreign Relations. He is currently the Editor-in-Chief of the Indian Journal of Artificial Intelligence & Law [e-ISSN:2582-6999] and the Editorin-Chief of The Indian Learning [e-ISSN: 2582-5631]. Suman Kalani Ms. Suman Kalani is an Assistant Professor at the Pravin Gandhi College of Law, Mumbai and is the Chief Research Expert (honorary) at the Indian Society of Artificial Intelligence & Law. She was invited as resource person on the topic “IPR and Human Rights - Issues and challenges” on 8th February, 2020 at the Refresher Course on Human Rights conducted by the UGC Human Resource Development Centre, University of Mumbai” from 3rd February to 18th February, 2020. She also had delivered a guest lecture for Copyright Law – Overview on 25th February, 2020, attended two-day programme on “Common Law vs. Chinese Law: Focus On Invisible Factors & Contexts That Are Relevant to Legal Practice” Organised by United School of Law, Karnavati University, was invited to deliver a guest session on ‘Intellectual Property Rights’ at Gurukul College of Commerce, Mumbai on 24th January, 2020. Akash Manwani Akash Manwani is a graduate from the University of Mumbai. He is currently the Chief Innovation Officer at the Indian Society of Artificial Intelligence and Law, an Executive

2020 Handbook on Artificial Intelligence and International Law

Board Member at Global Law Assembly by Internationalism, and also working in Maharashtra Tourism. Akash’s interests lie in the fields of international technology law, AI Policy and investment law. Kshitij Naik Kshitij Naik is the Chief Strategy Advisor at the Indian Society of Artificial Intelligence and Law. He is also the Chief Managing Editor of The Indian Learning [e-ISSN: 2582-5631] and a Senior Associate Editor for the Indian Journal of Artificial Intelligence & Law [e-ISSN:2582-6999]. Manohar Samal Manohar Samal is an Advocate qualified to practice in Indian courts and tribunals. He is a Research Analyst at Internationalism and also works for the South Asian Journal of International Law by Internationalism as an Associate Editor. He is also affiliated as an Analyst with Legal Atlas and is a part of the global legal team of the International Humanity Foundation. His areas of research include tax laws, securities laws, public international law, environment law and aviation law. Mridutpal Bhattacharya Mridutpal Bhattacharya is a Junior Research Analyst at the Indian Society of Artificial Intelligence and Law. He is also the Managing Editor of The Indian Learning [e-ISSN: 2582-5631] and a Junior Associate Editor for the Indian Journal of Artificial Intelligence & Law [e-ISSN:2582-6999]. His areas of interest include AI and Criminal Law & AI and International Relations. He also hosts the Policy Podcast, AI.Now for ISAIL. Arundhati Kale Arundhati is a student pursuing law from the Vivekananda Institute of Professional Studies. She is also a Junior Research Analyst at the Indian Society of Artificial Intelligence & Law. Dev Tejnani Mr. Dev Tejnani, a student currently pursuing law from the University of Mumbai, is a student of Vivekanand Education Society’s College of Law and is at present in his 4th Year of the Five Years Integrated Law Course. He is a Programme Coordinator of The Civilized AI, a Research Project/Programme run by the Indian Society of Artificial Intelligence & Law. He is an individual who is thoroughly interested in the area of Banking and Finance Investment Laws and wishes to specialise in the same. He intends to also carry out extensive research in the field of Artificial Intelligence and the growth of

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AI in the field of Finance. Sameer Samal Sameer Samal is a Junior Research Analyst at the Indian Society of Artificial Intelligence and Law and is pursuing law from Vivekanand Education Society’s College of Law, India. Saakshi Agarwal Saakshi Agarwal is a Research Contributor at the Indian Society of Artificial Intelligence and Law and is pursuing law from O.P. Jindal Global University (JGU), India. She has an avid interest in privacy laws and is fascinated by the impact that technological advancements would have in the on the laws as we know them. This interest led her to intern with ISAIL which helped her to gain considerable insight into this field. Aditi Sharma Aditi Sharma is a penultimate student at National Law University, Nagpur. Her interests and research areas include International Law, Internet Governance, International Diplomacy and Technology, Cyber Laws, Fintech, and Artificial Intelligence and Economics. She is currently Deputy Chief Research Officer of the Indian Society of Artificial Intelligence and Law, Senior Associate Editor of the Indian Journal of Artificial Intelligence and Law (e-ISSN: 2582-6999), and Managing Editor of The Indian Learning (e-ISSN: 2582-5631). Sanad Arora Sanad Arora, is pursuing law in O.P. Jindal Global University (JGU), India. He is currently working at the position of Junior Research Analysts at ISAIL, and his interests lie in the areas of AI & Law such as speech processing, vision, natural language processing & their regulation. Mayank Narang Mayank Narang is currently pursuing his law studies in O.P. Jindal Global University (JGU), India.

2020 Handbook on Artificial Intelligence and International Law

Section 1: Basis

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Introduction to AI Ethics & Education

Abhivardhan and Dev Tejnani

Introduction The introductory chapter is the door to the pathways of understanding and reasoning behind the role of and space for AI as a concept in law. Scholars in the field of law tend to argue often that anything which is AI, or artificial intelligence, as we call it, can be either a legal entity or a juristic entity. A legal entity means some entity, like a company, a human or even the state (government), which has some rights, powers, liabilities, privileges and duties. A juristic entity is simply some entity that is recognized under jurisprudence, but does not have a set of defined rights, duties or relationship with the state and any third party so forth. For example, any court precedent or circular/rules and regulations issued by a government can effectuate a loose de jure existence of the subject-entity as a juristic entity1. Generally, any product or service, which involves the technology of AI, will be based on machine learning, because AI is just an abstract concept in the field of technology (precisely mathematics). Let us cut some slack and then understand what is AI then. It also includes a dedicated section on AI and Education Defining Artificial Intelligence Let us analyse the popular and essential definitions of Artificial Intelligence from the fields of business and science. Following is a table which shows a schematic description of the definitions of AI given: Definition “… the science and engineering of making intelligent machines” … “[where] intelligence is the computational part of the ability to achieve goals in the world” “… making a machine behave in ways that would be called intelligent if a human were so behaving”

Proponent John McCarthy [original definition]

John McCarthy [alternative definition]

An alternative definition might be: A juristic entity or person is one in whom the law reposes rights or duties in its own name. 1

2020 Handbook on Artificial Intelligence and International Law

“… the science of making machines do things that would require intelligence if done by men” “… the science of making machines smart” “… is an intelligent machine” “… is the next, logical step in computing: a program that can figure out things for itself. It’s a program that can reprogram itself” “… anything a machine does to respond to its environment to maximize its chances of success” “… technologies emerging today that can understand, learn, and then act based on that information” “… anything that makes machines act more intelligently” “… a constellation of technologies that extend human capabilities by sensing, comprehending, acting and learning – allowing people to do much more” “… getting computers to do tasks that would normally require human intelligence” “… the ability of machines to exhibit human-like intelligence” “… a field of computer science that focuses on creating machines that can learn, recognize, predict, plan, and recommend — plus understand and respond to images and language “… a set of computer science techniques that enable systems to perform tasks normally requiring human intelligence” “… a computerized system that exhibits behavior that is commonly thought of as requiring intelligence” “… intelligence demonstrated by a machine or by software…[where] intelligence measures an agent’s general ability to achieve goals in a wide range of environments” “Artificial Intelligence is technology that behaves intelligently using skills

Marvin Minsky in 1968 Demis Hassabis Avinash Kaushik, Google Jim Sterne

Steven Struhl PwC India IBM Accenture

Deloitte McKinsey Salesforce

Economics Intelligence Unit US Government [NSTC] Calum Chase

Paul Marsden

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associated with human intelligence, including the ability to perceive, learn, reason and act autonomously.”

Now, the definitions clearly show that AI is a concept of something, which is non-human, is associated with human intelligence in competency & the uniqueness of such a ‘machine’ or system makes it AI. For the purposes of the Handbook, we propose a different definition here: Artificial Intelligence is field of technology in principle, concept or practice, which prioritizes on a system’s intelligence (in comparison with human intelligence) & ability to act or omit in an environment, where based on what, how and why it learns (through machine learning) enables itself to be expected to act or omit something. The reason why the words omit and act were used is that these terms are in direct corelation with the notions of law in terms of any legal or juristic entity. So, studying Artificial Intelligence requires us to understand how intelligence and action are scrutinized, and what narratives, theories and practical things are put into place around the idea of AI. AI is usually a technology which is intelligent in comparison to how we understand and have discovered human intelligence. Second, based on the assumption of the ‘machinic’ intelligence that AI is, we study the consequences and basis of the actions that lead to how AI should ethically behave in an environment. Studying this is all about AI Ethics as soft law in the field of Law, which means that a soft law is a code of ethics or compliance that must be followed or respected in order to ensure justice, fairness and accountability. For the purposes of this handbook, we need to understand that AI Ethics as a soft law which can be effectively used to bridge any gaps and incoherence between AI as a disruptive technology and the field of law (and for the purposes of this book, international law). Understanding Disruptive Technology and AI Disruptive technology is a subset of technology in a generation or a considerable time – which generally changes or influences the status quo of things done in the ordinary course of action. Based on the industry, its parameters, challenges and future, disruptive technologies take their own shape and have a special role in that particular industry. We can call it as a Disruptive Innovation as well, where unlike Sustainable Innovation, which either improves the status quo (evolutionary) or is unexpected, but does not alter the status quo (revolutionary), a Disruptive Innovation enables the case for replacement and

2020 Handbook on Artificial Intelligence and International Law

the arising need to establish and embrace alternatives. Artificial Intelligence, by all means, is a Disruptive Technology/Innovation, because it makes the case for replaceability in an industry, and even affects the industry’s notions over methodology of things. AI does affect many key industries whether through a product or a service, and there are case studies, which show it can even replace things which exist.

Theoretical Underpinnings We will discuss the following theories related to artificial intelligence that will be essential for a reader to understand what will be dealt further in the chapters in general: 1. The Turing Test 2. The Dartmouth Principles on AI 3. Technology Distancing 4. Anthropomorphism and Ecocentrism 5. Consequentialism The Turing Test The Turing test, originally known as the Imitation Game (also known as the Polite Convention Theory) by Alan Turing, explains and lays the primary foundations of AI Ethics, and the synergy of mathematics & ethics. The idea is quite simple. Now, Rene Descartes in 1637, in his works on ‘Automata’, tried to suggest that an automaton can think and behave autonomously. Turing, in his 1950 paper specifically focuses on machinic intelligence, which is a followup to the Turing Test. Now, the concept simply means as follows: • Imagine there are 3 entities: A, B and C. A is an Interrogator, a human, who is given a task to determine among entities B and C, which is actually human; • Now, A does not know that B is a computer while C is a human; • He passes on commands or messages and then communicates with both the entities with the same message or command; • C’s response would be of a human for sure, but the way B responds decides the following: ─ Whether B is ‘intelligent’ enough to pretend or behave like a human; ─ Whether the finite features of B are enough to convince A, the interrogator, that B is a human, since A does not know anything about the identity of B & C from the beginning;

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That is how the Turing test works. Now, the explanation given above is the simplest and elementary one, because there are variants and bi-products of the same Turing Test developed. The concept however is not absolute, nor complete, because of several reasons: • The test is just a primary means to understand how a machine can befool a person to show whether the machine is a human or not. Based on the degrees and kinds of expressions made, this can emulate accordingly the same to some extent. However, the test does not answer any question to the basic anthropomorphic limits of understanding of intelligence; • The portion of intelligence that an AI/ML system must possess is again inspired from theories and researches central to human intelligence, and the perception cum analogies that propound the idea of what human intelligence really is. Science is not perfect, and despite the fact that the fields of cybernetics and neurotics are already engaged in determining what can be ‘intelligence’ for the AI system, it is clear that the test is just the tip of the iceberg. • The philosophy of AI is quite deterministic to influence the science behind developing AI. However, the principle is much naïve and cannot influence the practical developments in the field of AI Research effectively. Yet, this test is important to be understood, because it gives an understanding of the basic end-goals which were proposed in order to begin with the idea of AI anyways. The Dartmouth Principles on AI The United States, in the times of the Cold War had started dominating the narratives and research on the idea of AI. In 1956, the Dartmouth Summer Research Project on Artificial Intelligence, a workshop for a period of 8 weeks, was attended by Nathan Rochester, Claude Shannon and Marvin Minsky with others, where suggestively, the original ideas related to AI Ethics had borne. John McCarthy had originally organized this workshop "to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." In fact, the term Artificial Intelligence was discovered in this workshop for the first time, where learning, and its methods, components and forms became the important milestone for the purpose of reasonable study. The proposal submitted by McCarthy, Shannon, Minsky and Rochester explains the following: • The proponents believed that humans were unable to write programs that can reinforce simulations of the human brain, and that the AI system, or

•

• •

2020 Handbook on Artificial Intelligence and International Law

computer as we call it, would not be able to understand how they simulations work, let alone the fact that they would even not be able to emulate it anyways; The proponents believed that it is important to assess how semantics of a language and the implied value of the sentences in a language is determined by a computer. They proposed such was not researched deeply at that time, which was true in those times; The idea of simulating the neural system of a human being was proposed by the concept of neuron nets (now known as Neural Networks), where the scientists stated that effective research is necessary to discover how the capabilities of neurons be emulated into a computer; The idea that an AI system must self-improve itself was also proposed in this 1956 workshop by the scientists, which was in those days, a mere conjecture; Additionally, it was proposed that “the difference between creative thinking and unimaginative competent thinking lies in the injection of” some sort of randomness. That educated guess/intuition includes some randomness controlled, which is put into thought;

The Dartmouth Workshop is essential for students and readers of AI Ethics to understand how the aesthetics, ethics and rationale behind developing AI Ethics was formed in the early times. Technology Distancing The concept is quite simple. Technology distancing means that when any innovation (technological) replaces, evolves or revolutionizes anything which exists as some status quo, then based on the skill and features of the technology innovation, the human user would generally be more distanced from manual activities as we define. For example, computers and laptops enabled humans to invest time in writing effectively, and let the computer handle format adjustment, sharing and other complex activities, replacing old printers from the 14th century to the 19th century and even the typewriters. Emails and the practice of internet significantly improved communication and messaging, and revolutionized the way communication works. Now, people do not require to send messages through sealed envelopes too much. Office work, government ID verification and trademark verification work became online, thanks to the phenomenon of technology distancing. Bullet trains replaced the coal-based engines, while cars replaced horse carts earlier. The inference here is that technology enables change or improvement of the status quo, but it also distances any manual process that humans are expected to do. Using AI involves that idea that technology distances humans from doing manual

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activities. For example, Facebook and Twitter need algorithmic tools to handle social media users and their content posted every second, every day. They do not need humans to handle millions of posts/tweets published every single time. The idea of procedural regularity and automation also comes through this only. Therefore, it is important to understand that AI Ethics involves distancing human beings from manual activities. But there is an important angle to such form of distancing. Any manual activity involves the anthropological realities, constraints and abilities possessed by the human, who is involved in the activity. For example, a sanitation worker, who has to clean manholes, manually, without any external assistance, knows and experiences how he manually handles the task. An external system would be a reasonable replacement to assist him or replace him in the task, but the experience and the way the experience is received is completely the man’s possession and attribute, which cannot be replicated by that system much. The system might be more effective and accurate, but the aesthetics of experience would be much different here. This also leads us to ask a question: does it matter how machines are expected to perceive, and so expected to act? Always ask this question when hypes are being spread on the efficacy and features of an AI/ML product or service. Anthropomorphism and Ecocentrism Anthropomorphism and Ecocentrism are two different theories in the fields of anthropology and political science. The former simply means the attribution of some human traits, qualities or characteristics to some entity, which is nonhuman. The previous sub-section of this chapter was all about technology distancing. In continuation to the same issue, let us understand anthropomorphism simply. The idea is that any qualities, traits, experiences, characteristics or skills which are possessed by a human being, if are attributed to any non-human entity, then it is anthropomorphism. In the field of literature, we call it personification. Here is an interesting reference: [A]nd there is another charm about him, namely, that he puts animals in a pleasing light and makes them interesting to mankind. For after being brought up from childhood with these stories, and after being as it were nursed by them from babyhood, we acquire certain opinions of the several animals and think of some of them as royal animals, of others as silly, of others as witty, and others as innocent. - Apollonius of Tyana (Flavius Philostratus (c. 210 CE), 1912)

This reference shows how clearly, animals have been personified, and their outlook has been humanized for some sort of inspiration or imitation. The above example is nothing but a clear anthropomorphic motif. You can find more references in poems and epics, where such motifs are envisioned by the

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authors/poets. Similarly, in AI Ethics, the idea of technology distancing transcends to the question of personifying AI and influencing the roles, responsibilities, capabilities and position of human beings in various environments. For AI Ethicists and practitioners, anthropomorphism is important to be understood because when we would interpret the legal or juristic characteristics of AI systems, and the agency cum liabilities, then understanding how and why such products and services are put into use to see how developers and companies can be held to account for any anthropomorphic influence they have over human users, would be useful for understanding the due diligence measures that can be developed for AI services and systems, based on their types, as another angle of introspection when it comes human rights and technology ethics. The latter, ecocentrism – is a political ideology in the field of environmental politics. Now, the term signifies the idea of have environment-related laws and policies, which are central to the natural environment, and not human beings. Therefore, ecocentrism is nature-centric, opposed to the idea of anthropocentrism, which means human-centric political philosophies. Although ecocentrism has its own fallacies, and is not a perfect political philosophy, it still has some reflective importance in understanding policy paralyses. So, ecocentrism focuses on a critical re-evaluation of the relationship between humanity and nature. It also means that the ages of technology advancement have been put into question through the idea of ecocentrism, because technological advancement anyways affects the ecological order or the natural ecosystem of Earth as a planet. However, it is debatable as to how the influence surmounts, because in the case climate change, for example, the phenomenon is cyclic. Climate change and its impact has been happening for a long time, which cannot be alone estimated by some parameters of assessment. Therefore, just because industrialization and capitalism is connected to economic development, and industrial activities contribute to global warming and other phenomenon, it is not necessary to assume that correlation between technological advancement and climate change is clear. There are debates and researches happening contrary to the claims being made, which are sometimes reasonable, but most of the time, are sweeping and unreliable. Another angle of ecocentrism, which we must take into consideration, is the historical side behind the political theory. Ecocentrism is critical of technology, but is also critical of the Western culture and societies. The anthropocentric development of the Western civilization has been put into question when technology ethics and environment studies became prevalent in the 2010s. The philosophy does affect AI Ethics in this way. Since the theories of reason and culture, behind the scientific and practical aspects of AI and its role in a human society are mostly taken from Western thinkers and leaders, ecocentrism

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questions the biases behind the theoretical undertakings of AI Ethics as a soft law. Consequentialism

Consequentialism is a set of similar political, legal and ethical ideas, which have been nurtured and transformed since ages. It is a teleological theory, which focuses on the idea of utilitarianism, earlier founded by Jeremy Bentham. Bentham explains the idea of utilitarianism as follows: [N]ature has placed mankind under the governance of two sovereign masters, pain and pleasure. It is for them alone to point out what we ought to do, as well as to determine what we shall do. On the one hand the standard of right and wrong, on the other the chain of causes and effects, are fastened to their throne. They govern us in all we do, in all we say, in all we think... (Bentham, 1789)

Hedonism follows the concept of pain and pleasure, and states very clearly, that seeking pleasure and avoiding suffering are the only components of well-being. In a follow-up, Peter Singer proposes the idea of preference utilitarianism, where he proposes to maximise the domains of what is the pleasure sought, and what is the suffering that would be ought to be avoided. In simple terms, utilitarianism or consequentialism generally emphasizes on the notion that there are two basic things for any human being in a life, which constitute the effect and reason of the action committed by that human being: pain and pleasure. Philosophers argue that in order to seek more pleasure and avoid any tantamount pain, the human emphasizes on the idea of utility. Similarly, the concept of utilitarianism has to do with AI in a significant manner, because the role of AI as a product or a service can then be easily defined. There are some other kinds of consequentialisms as well, which affect our understanding of policy-making and rule of law: 1. Rule Consequentialism: It is the rules which define the consequences. Better the rules are preserved and respected, better the consequences are or might be; 2. State Consequentialism: It is the larger narrative of interest which is connected to the idea of either a collective or a state. So, a religious monarchy, for example would centralize the notion of welfare for a divine cause, but a state, which separates itself from religion, would focus on the people’s interest, and the government’s interests to centralize the notion of welfare or any effects of some cause. Here, the idea of collective or state interest drives consequences; 3. Motive Consequentialism: It is the motive which drives and defines the consequences of the state of affairs we live in. The focal point of

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utilitarianism and judging an act as good or bad is therefore reliant on how right or wrong the motive is; 4. Negative Consequentialism: It is the worse consequence which has to be avoided. It simply means that the idea of utilitarianism is fixated to prevent/avoid anything bad or painful or wrong from happening; Consequentialism affects AI in the following ways: • Since we treat AI Ethics as some soft law to determine the framework of liabilities, accountabilities and responsibilities, the theories of consequentialism help us determine on a case-to-case basis to establish legal and policy issues. This may range from corporate compliance to customer support and maybe even to issues related to automated discrimination & fairness; • Consequentialism is not affected merely by the theories postulated by jurists in the West: based on the kind of political and legal system, consequentialism is important to assert and question the role and position of the affected parties, the state and the third parties wherever possible. The idea of consequentialism also opens the case to ensure how curfews, lockdowns and other state-sponsored restrictions are handled. In cyberspace, consequentialism is helpful in determining how misinformation is being tackled and investigated to understand crime patterns and the scope of information & perception warfare; • Consequentialism and its theories seem to be very simple in theory, but their application differs and can have complex vertical and horizontal hierarchies. For example, the idea of privacy in AI Governance can be treated as some sort of strategic affair for companies and researchers;

Applied AI Ethics: Soft Law This section covers the application and relevance of AI Ethics as a soft law & also the important conceptions within the realm of AI Ethics from the industrial, juridical and scholarly points of view. AI Ethics as Soft Law AI Ethics is the field, which establishes various principles of ethics – and checks its applicability cum reasonability, to ensure fairness, transparency and accountability. If we read the EDW Paper on the Asilomar Principles on AI of 2017, or the General Data Protection Regulation of the European Union to start with, we can easily understand that AI as a system/product/service is

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being considered as an agent entity, i.e., the entity behind the development, maintenance, compliance assurance and production of the AI is held accountable for actions. The domain of digital rights is developed in civil and criminal law to assure the people that any kind of accountability related to artificial intelligence is upheld. As discussed in the previous sections, the notions of law, politics and ethics have a clear role to ascertain and support the cause behind the existence and practice of such laws, rules and regulations. In the backdrop, the concepts of technology distancing and consequentialism are essential to define AI Ethics as a soft law. Let us dive deeper into the notion of AI Ethics as a ‘soft law’. The definition of soft law is loosely defined by ECCHR as follows: The term soft law is used to denote agreements, principles and declarations that are not legally binding. Soft law instruments are predominantly found in the international sphere. UN General Assembly resolutions are an example of soft law. Hard law refers generally to legal obligations that are binding on the parties involved and which can be legally enforced before a court (ECCHR).

The features of AI Ethics as a soft law are enumerated as follows: • The concepts of fairness, transparency and accountability in AI Ethics may or may not be binding, but based on application and use, they might have a significant value; • Based on the needs of the industry, specific guidelines and measures can be established for ensuring compliance, credibility and authoritative research; • The actors who are involved in the creation, production and maintenance of any product or service where some AI/ML tools are involved, are many (Council of Europe, 2019). The principle is popularly known as the ‘many hands’ principle, where developers, creative designers, UI handlers, lawyers, company executives and other relevant actors are held accountable to their contribution to ensure the credibility of the AI system; • AI Ethics is highly dependent on the kind of industry it is involved with & the cultural and anthropological aspects of the society and individual spaces which encourage the industries to sustain; • Some aspect of AI Ethics is also based on information warfare, because of the fact that much hype (Canon; Funk, 2019) which is created on the use and impact of AI as a product/service decently affects the market conditions. Even due to a great lack of research on the effectiveness of AI, despite the fact that the quantity of AI papers published has increased much since 2017, narratives are made which may affect justice administration and rule of law in ascertaining the role of AI Ethics as a soft law; It is therefore reasonable to ascertain that AI Ethics as a soft law would be

2020 Handbook on Artificial Intelligence and International Law

helpful enough to render approaches towards ideation in matters related to policymaking. Let us now understand the kinds of conceptions and phenomena built under the realm of AI Ethics, which are useful for the industries, legal systems and scholarships that are affected. Life-Cycle of AI It is important to understand every AI/ML system has a proper life-cycle, which developers and scientists generally follow to ensure that compliance, effectiveness and transparency are adequately achieved. Some of the common phases of the life-cycle of AI, in terms of its development, maintenance and production are enumerated as follows: 1. Define Project Objectives: It is very important to ensure the clearest and certain objectives of a project taken up to develop an AI/ML product/service. Without clear and reasonable objects, the project would not be funded or supported either, or maybe would not have adequate scope. For example, some facial recognition software can be developed to ensure thermal screening of passengers in the airport before and after a flight has taken off, to ensure reasonable information about the chances of their being sick of COVID19 is detected; 2. Acquire and Explore Data: Data acquisition and exploration is the next stage after clear and certain objectives have been framed. Now, you are required to allot some finite parameters in consultation with the stakeholders who are involved in the environment for which the AI/ML thing has to be created and prepared; 3. Model Data: Now, in order to gain insights from your ML realm, you must determine some ‘target’ variable, which will give you a deeper understanding of the trends, efficacy and biases in the data & algorithmic framework; 4. Interpret and Communicate: ML realms are considered as ‘black box’, and it is already under consideration in various D9 member states like the US, India and EU member-states that the ML system should not be like a black box, which means it lacks interpretation and explanation effectively. Therefore, it is highly important that the ML system is sustainable in terms of interpretation and explanability; 5. Implement, Document, and Maintain: It is important that the data project through ML is implemented, documented and maintained;

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Algorithmic Policing Algorithmic Policing is the practice of monitoring and surveilling any environment and a defined set of activities that happen in that environment using algorithms. Now, the way the activities are monitored using algorithms might differ but there is no doubt in accepting the fact that activities can be either done in an ordinary course as default, or might be spontaneous, which are not easily predictable. Algorithmic policing surely is therefore helpful but can be harmful if is not utilized reasonably. Generally, surveillance activities, private censorship on social media platforms, health-related surveillance, recognition software-based verification and other related activities come in the scope of algorithmic policing. It is therefore important to understand that algorithms-based policing of activities must be based on transparent enquiry, and the use of such technology must be reasonably scrutinized in order to avoid any mishandling, considering the delicate nature of the practice. Automated Discrimination/Fairness A paper authored by Sandra Wachter, Brent Mittelstadt & Chris Russell (Wachter, et al., 2020) on the European Law related to non-discrimination shows that fairness, a concept of law – cannot be automated in practice, if is assumed to be. Let us understand automated discrimination then first, in a proper manner. • Automated services, which are used in various industry sectors, sometimes – due to algorithmic and/or data-related biases, are not able to act in a reasonable graph of evaluation, leading to discriminatory activities in making choices or decisions – which could have been effective. This is what we call automated discrimination; • Automated discrimination is not limited to employment-related evaluation via automated systems. Its scope can exist in the judiciary, medical and health sector & other economic and governance sectors; • Automated discrimination also occurs because of the AI-related hype, that scientists assume the potential of AI/ML systems to be self-sufficient by almost all means. It has to do with the hype where the AI/ML systems lack effectiveness and strategic capabilities to overcome or explain their biases; Kinds of Machine Learning Here is a glossary of the important kinds of machine learning, and their interpretations provided in a table:

2020 Handbook on Artificial Intelligence and International Law

Kind of Machine Learning Defined Supervised Learning: “Applications in which the training data comprises examples of the input vectors along with their corresponding target vectors are known as supervised learning problems. (Bishop, 2006 p. 3)” Unsupervised Learning: “In unsupervised learning, there is no instructor or teacher, and the algorithm must learn to make sense of the data without this guide (Goodfellow, et al., 2016 p. 105).” Reinforcement Learning: “Reinforcement learning is learning what to do — how to map situations to actions—so as to maximize a numerical reward signal. The learner is not told which actions to take, but instead must discover which actions yield the most reward by trying them (Sutton, et al., 2018 p. 1).” Semi-Supervised Learning: “In semisupervised learning we are given a few labeled examples and must make what we can of a large collection of unlabeled examples. Even the labels themselves may not be the oracular truths that we hope for (Russell, et al., 2015 p. 295).” Self-Supervised Learning: “The selfsupervised learning framework requires only unlabeled data in order to formulate a pretext learning task such as predicting context or image rotation, for which a target objective can be computed without supervision (Kolesnikove, et al., 2019).” Ensemble Learning: “The field of ensemble learning provides many ways of combining the ensemble members’ predictions, including uniform weighting and weights chosen on a validation set (Goodfellow, et al., 2016 p. 472).”

Interpretation Here the input and target vectors are correspondingly finite in terms of the learning mechanism. It is highly supervised, and the correspondence between input and target or output is supervised. No supervision is granted. The correspondence does not exist.

Here whatever the object of learning is, RL enables to reward the ML system for the action, and maximises the reward. No specific actions are ordered, but are aspired by a reward-based reinforcement to act reasonably as the person wants.

Few examples are given to learn to a system, which also means that partial supervision is granted.

They are generally used for increasing and utilizing predictive tasks through ML systems.

This is an approach where 2 or more modes are fit on the same data & the predictions from each model are combined.

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Adversarial Machine Learning: “Adversarial examples exploit the way artificial intelligence algorithms work to disrupt the behavior of artificial intelligence algorithms. In the past few years, adversarial machine learning has become an active area of research as the role of AI continues to grow in many of the applications we use (Dickson, 2020).”

Such approaches are used to disrupt the ordinary course of nature of an AI’s algorithmic activities, thereby disrupting the behaviour of the algorithms.

Natural Language Interpretation v. Natural Language Processing It is important to understand the difference between Natural Language Interpretation (NLU) and National Language Processing (NLP). 1. NLP is an intersectional field in AI, which transcends data mining, computer science and AI at the same time. The focus of NLP is to ensure how to process large amounts of natural language data. Facial recognition systems and chatbots work under the domain of NLP; 2. NLU is a subset of NLP, and is narrowly focused on machine reading comprehension. It is through effective NLU that a successful NLP is achieved. The process seems to be similar, but NLU is different as it is specific to the comprehension of information for an ML system. Archiving, gathering and discovering information is achieved through NLU; Responsible Artificial Intelligence The idea of Responsible AI is that any AI/ML system must be responsible ab initio, in terms of action and collaboration. The term is generally used in the context of governance and has more to do with considering an agency of companies, in order to ensure relevant compliance from them to achieve suitable AI products and services; Explainable Artificial Intelligence Explainable AI (XAI) is a concept, which deals with a problem of mathematics and AI, known as the interpretability problem. As discussed, ML realms must not be black box, i.e., they must be interpretive and capable enough to explain how they work, how they learn, and why their functioning persists likewise. • The idea of mutual trust stems with the concept of XAI, where humans and the AI/ML systems considered under some agency (of liability) must

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cooperate in a relevant environment. XAI therefore is not just limited to mathematics, but has attracted AI Ethicists too; • The European GDPR includes a provision of Right to Explanation in the Article 22 of the Regulation, which for example is limited to local interpretability. It also concedes the many hands principle, because explanability ensures a chain of accountability framework for any wrongful act caused due to the AI/ML system. It also ensures that the life-cycle of the system is put into checks and balances; Artificial General Intelligence It is an abstract conception, where AI has attained a surmounted level of learning and explanability, which is unbeatable, and is invincible, scientifically and deontologically. There is no possibility of AGI for now, but theories and science-fictions are for sure written where the social, historical, political and legal aspects of AGI are being generally discussed; Algorithmic Diplomacy & Warfare Algorithms generally can be used to ensure means of warfare in cyberspace (disinformation warfare, cybercrimes, etc.) or in physical space again through algorithmic activities. Currently there is no regulation of algorithms-based warfare in international law. Using data of users, whether any, to feed algorithms in order to cause private censorship is also algorithmic warfare. Algorithmic diplomacy therefore diplomatic negotiations on the basis of policy, legal, strategic and political intervention of algorithms. Bargaining and conciliation, often in the matters of fintech-based trade, or IP rights or digital rights can be done using this generation of diplomacy considering the integral role of AI/ML systems in the foreign relations between countries and non-state actors.

AI and Education: Basis and Relevance Education Aesthetics and Thought Leadership

Education rights are not limited to the mere component of generalizing it in various human rights instruments in international law. Right to education has a consequentialist take, where issues like illiteracy, disinformation and even the case of being unskilled is generally being put into question. However, with an aesthetic perspective, education rights should be seen from a libertarian perspective, which is more important in the situation where AI Ethics is

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influential to affect choice rights and individual civil liberties. Therefore, thought leadership and the aesthetics of education should be seen with a policy perspective, because beyond giving moral recognition and legal sanction of right to education, improvement of quality and effectiveness in pedagogy involves a human touch to things, which through AI might be lost in translation. • Individual and personal liberties such as thought leadership and creative wisdom must be sincerely protected and ensured, not through coercive legal measures, but through effective policy solutions in a systemic manner; • The intersectional role of management sciences, social sciences and special sciences enables thought leadership and education activities in the field of AI Ethics principally. Now, a basic model of learning and education in AI Ethics and Policy to include other important fields, which we add up or mix with, can be in the form of AI+X, where X means any field possible, which can be put into parallel use with AI Ethics; • Since we are seeing that the ethics of AI will be transfused into other disciplines, it raises questions about the pedagogy, which is being used, and how the academic process is underway; • It is important to focus on individual skill development and its clear enhancement because without a clear policy undertaking on it, it seems virtually passive to protect creative wisdom and thought leadership; When it comes to the aesthetic purpose and scope of thought leadership, it has to be accepted that decision-shaping and decision-making take time in their own nuanced ways. Human autonomy and individual liberty with regards to educative leadership has to be certainly protected and affirmed, in order to ensure that education policies and approaches are human-centric. There are narratives and issues of utmost hypes on AI Education, and the usage of AI in education, which must be effectively addressed. Artificial Intelligence has gained immense momentum in the last few years, especially the usage of Artificial Intelligence in the field of Education, which has witnessed a tremendous boost in the last twenty-five years. It is imperative to understand that in the last twenty-five years, Artificial Intelligence in Education, i.e., AIED has been bolstering resources in order to solve a twofold problem, wherein innovators are striving to create systems which could be deemed to be regarded at par with the humans and the way human teachers confer or impart knowledge. It could be said that the thinkers who are bolstering resources in the AIED field are working towards getting a solution to a two-sigma problem (The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems, 2011). It is quite

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pertinent to note that the use of AI has significantly developed and there have been innumerable developments. For instance, interactive learning environment (ILE) has been given a lot of importance and on the basis of analysis and the papers provided by ILE, there has been a huge development in the field of Education which specific emphasis on creating an efficient platform for tutors enabling them to demonstrate their lectures in a manner that consumes less time, however the knowledge that is imparted has a much greater significance (Is Over Practice Necessary? - improving learning efficiency with the cognitive tutor through Educational Data Mining, 2007 pp. 158, 511). However, with the growth and the advancement in technology and with the commencement of 21st Century skills (Trilling, et al., 2009) and the Next Generation Science Standards (NGSS, 2013) have stressed upon the increased usage of general learning skills and specifically elucidate upon how important it is to deal with the various aspects pertaining to metacognition, critical thinking, and collaboration. In fact, it is imperative to note that in the present educational environment, a lot of organizations and schools strive towards bolstering their resources thereby incorporating authenticity in their practice areas striving to solve huge problems or gaps in the educational sector. In order to ensure that AIED does not lose its relevance, it is imperative that AI mechanisms develop in the field of Education and it imperative that the education sector adapts to changes quickly and makes the most of it.

The Basis The usage of Artificial Intelligence in the field of Education has been one of the core areas when it comes to carrying out academic research and it is imperative to note that the application of artificial intelligence in imparting knowledge has been a core area of practice since the last thirty years. The various tools of AIED are aimed towards bolstering the way education is imparted in the traditional classrooms, workplaces, formal educational centres and lifelong learning centres. AI can be deemed to be regarded as the binding force which inculcates the usage of technology and it in itself is an interdisciplinary when it comes to understanding learning sciences such as education, psychology, neurosciences, linguistics, sociology and anthropology inter alia promoting the development of a learning environment which can be deemed to be regarded as an adaptive learning environment. This adaptive learning environment can be made possible with the innumerable developments that are undertaken in the field of AI and Education, since AIED tools can be deemed to be regarded as extremely personalized, engaging,

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flexible and by far, the most effective when it comes to imparting effective knowledge.2 It is imperative to understand that the main purpose of AIED tools is to make sure that, “computationally precise and explicit forms of educational, psychological and social knowledge which are often left implicit are given special attention” (The Defining Characteristics of Intelligent Tutoring Systems Re-search: ITSs Care, Precisely, 1999). This basically means that apart from being “smart”, education technology can be made an extremely powerful tool, which could open a Pandora’s box and this could be deemed to be regarded as the “black box of learning”. The “black box of learning” can be deemed to be regarded as an aspect which can provide individuals in the field of education a deeper and a greater understanding of how the various processes with regards to imparting knowledge takes place, for instance, how learning can have an impact on the socio-economic or physical being of an individual. The various understandings pertaining to this aspect can then be adhered to when it comes to developing a software which specifically makes use of AI algorithms in order to create an AIED software, which can help an individual see and understand the various steps that an individual learner or a student goes through whilst studying a subject, for instance, physics or what issues he/she may face whilst studying a subject (The Andes Physics Tutoring System: Lessons Learned, 2005). These issues when taken into consideration can help tutors understand in a much simpler and easier manner as to where an individual student is facing difficulty and enable the tutors to efficiently address these issues. AI revolves around computer and computer software’s which programmed in a specific way that requires the assistance of a human, therefore, it can safely be said that AI requires a two-fold knowledge application of knowledge, that is the programming of algorithms and the ability of an individual to swiftly and precisely process the knowledge of running the algorithm. Now, an AIED system is based on various models which can be deemed to be regarded as an integrated system consisting of three models which can be deemed to be regarded as the pedagogical model, the domain model, and the learner model. The Pedagogical Model can be deemed to be regarded as the model that deals with the aspects pertaining to knowledge and the expertise that goes into it. For instance, dealing with the aspects pertaining to “productive failure”, which enables a student to understand and go through a concept and also allows the student to make mistakes and understand whether the answer input by him or Adaptive Learning Environment- An adaptive learning environment can be deemed to be regarded as a digital learning environment that has the capacity to make sure that the purposes of teaching and learning coherently work in order to ensure that the needs of all individual learners are adhered to. 2

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her is the “right” answer or not, furthermore, it can also comprise of aspects pertaining to the various actions taken by students, which are in turn capable of providing the students an insight into whether they are improving their skills while learning and understanding the core concepts pertaining to a subject that they are learning. The Domain Model can be deemed to be regarded as the model that deals with the aspects pertaining to the knowledge with regards to the subject that is being taught, i.e. the expertise which goes into creating a domain. The Domain model could for instance, deal with the various technical subjects, like mathematics and science. It can perform functions such as addition, subtraction or multiplication of two fractions, furthermore, it can also work and guide an individual student upon how to structure a perfectly valid argument. The Domain Model can also enable an individual to understand the various approaches that he or she may take when it comes to reading a text, i.e. sensing or for understanding the various approaches pertaining to reading and analyzing a text. The Learner Model can be deemed to be regarded as the model that deals with the aspects pertaining to the knowledge and the expertise that a learner may already possess. This model can be adhered to in understanding an individual student’s achievements that he/she has made in the past and also the difficulties that the student may face whilst learning or analyzing a chapter or a particular subject. The model can also enable the tutors to understand the emotional state of a student while he/she is learning in order to understand and analyse whether the student is grasping the information which is being furnished to him or her as it is extremely imperative to understand the mind-set of the child or the student when it comes to analyzing a particular subject. The Learner model can also have the capacity to analyse and understand whether the student is engaging in the subject-matter that is being taught to him or her and it is extremely necessary for the tutors or the professors to analyse whether the student is finishing a particular task on time. All these models are not implemented; however, they could be put to use while a school or a university or an organization makes use of AI integrated systems when it comes to delivering knowledge as AI could successfully supplement teachers and enable the teachers also to analyse and work in a much comprehensive and in a much better manner, thereby enabling them to clearly analyse whether whatever knowledge is being imparted upon the students is being understood by them or not. If one delves into these aspects, it’ll be easier to understand that these models are mere examples and these learner models are just different or distinct ways when it comes to representing and showing the various interactions that can take place between an individual and the AI powered system. The interactions which these above-mentioned models represent, may successfully enable the tutors to understand the emotional state,

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the past achievements, the activities that an individual student undertakes inter alia learning the basic subjects or the core areas of his or her study. Basically, these interactions can enable the tutors to analyse and determine whether these components can be adhered to or not and whether they can be used by the domain or the pedagogy components of an AIEd programme when it comes to understanding and analyzing if the teacher and the student are succeeding in the way the course has been structured. It is imperative to understand that the domain and the pedagogy models which have been referred to above also adhere to information when it comes to extracting the most appropriate interactions, i.e. the necessary learning materials or the activities that are undertaken while learning. It is extremely imperative to understand that the activities undertaken by a learner are constantly put back into the learner model, which enables the algorithm to develop by itself and make itself more user friendly, analyzing what exactly the student requires. In a nutshell, it could be said that the use of this model can basically render a custom, or a tailor-made programme for an individual since it understands the activities undertaken by a learner and then constantly puts it back into the learner model, enabling the system to become smarter and robust.

Case Studies on AI & Education At present, there are innumerable AI operated Educational applications which are already being used by a number of schools and universities. A number of universities and schools are already doing a great job by making use of various AIED models and make use of, “educational data mining” (EDM)3. This model can be deemed to be regarded as a comprehensive model which has the capacity to analyse the behavioural patterns of students, for instance, calculating and analyzing whether a student is attending a class regularly or not and whether the assignments allotted to such a student are being completed by him in a time-bound manner or not. The system then intimates the teachers whether the student is completing his assignments in a timely manner or not and if he/she is not, then the system alerts the teachers to pay special attention to such students who may possibly abandon their studies or their course. There are a number of AI researchers who are bolstering their resources in order to analyse and understand different user interfaces, for instance, making use of natural language processing, speech and gesture recognition, eyetracking, and other physiological sensors, which can be used in order to support With the advancements and the use of new and robust methods, ‘big data’ analysis has become easier. ‘Big data’ can be interpreted from autonomous computer based systems and these systems are being used by schools, colleges, universities whilst carrying out their administrative and managerial functions. 3

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AIED software’s and software’s do not specifically relate to AIED. It is imperative to throw light upon the kinds of AIDED software which could be developed and which could be specifically designed in order to understand whether a student is understanding a subject collaboratively or whether he or she requires a personal tutor. At the same time, the AIED software can also be designed in such a manner that it enables an all-round support to the virtual reality and also enables every tutor to understand how an intelligent AI system works when it comes to imparting knowledge. AIED can be deemed to be regarded as a personal tutor for each and every student: It is always better for a student or a learner to have a personal guide or a personal mentor who simply focuses on him or her and enables the learner to grow. One-on-one teaching is always deemed to be regarded as effective and efficient since the student can open up and the teacher or the tutor efficiently cater to the needs, the specific needs of the child who he or she is teaching. However, with the ever-growing competition in each field, one-on-one teaching has been lost and it cannot be feasible for each tutor to separately impart knowledge to each and every student, in fact, there may not be enough tutors that can be allotted to each student, even if there are enough teachers, it cannot be deemed to be regarded as a feasible option since providing salaries to each and every tutor would be an extremely expensive affair. Therefore, this is where Intelligent Tutoring Systems come into picture, i.e. ITS. Now, ITS can be said to be using AI algorithms which enable a student to simulate the oneon-one teaching experience, when it comes to delivering a lecture or understanding a student’s needs or analyzing what the student is expecting out of his or her teacher and all of this can be made possible without any human interaction or any human teacher being present there. ITS learners can be deemed to be regarded as those who are in complete control of the way they absorb information and this enables a student to learn and analyse whether they are developing the skills of self-regulation or not; however, some learners adhere to strategies which are pedagogical in nature, which enables the learner to acquire information which is apt and supported properly. This basically can be deemed to be regarded as the scaffolding of the learning process.4 The year 1970 first saw the advent of some of the first and the foremost systems which ran on AI algorithms and they had the capacity to provide a customized and an adaptive format of instruction. For instance, the ‘BUGGY (Diagnostic Models for Procedural Bugs in Basic Mathematical Skills, 1978)’, which was deemed to be regarded as a system which was way ahead of its time and this When it comes to education, the aspects with regards to scaffolding can be deemed to be regarded as a method of analyzing an individual learner’s problems, enabling him/her to carry out a task, achieve a goal or reach to a point wherein they can achieve a goal through gradually scaling ahead without any sorts of assistance. 4

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system was incorporated in order to teach the basic functions of mathematics, i.e. addition and subtraction. The BUGGY consisted of a model which specifically focused on the possible errors or misconceptions that an individual learner could have whilst carrying out the basic mathematical functions, for instance, procedural arithmetic. The “bug library” was deemed to be regarded as one of the main domains of the system and the model upon which the system was created, and it was used by the tutors in order to analyse and understand the errors that an individual student made, enabling the tutors to better understand where their students were going wrong and enabling them to cater to the specific needs and the specific areas of difficulties that the students were facing. In the beginning, it contained innumerable bugs, which had the capacity to understand and analyse these aspects, and these bugs were interestingly programmed into the code of the system. Over a period of time, however, these misconceptions were cleared and they were then added into the libraries. However, in the present times, a number of organizations make use of machine learning techniques and instead of adhering to the creation of models, they use the Intelligent Tutoring Systems with the help of machine learning tools and they programme algorithms which have the capacity to learn things by itself. These self-training algorithms are based on a number of data sets and they consist of neural networks, which have the capacity to take decisions which can be deemed to be regarded as appropriate and enable the tutors to take decisions in a timely manner, providing the apt learning content to their learners. However, it is extremely imperative to note that when an organization adheres to this approach, it can be deemed to be regarded as an extremely difficult approach since the rationale behind these decisions can be deemed to be regarded as thoroughly explicit in nature. Furthermore, Modern model-based systems which are adaptive in nature can be deemed to be regarded as much more flexible and much more comprehensive in nature. They have the capacity to enable the rationale with regards to each decision that is taken by a particular system, which needs to be made in such a way that it can be understood by humans and therefore enabling the tutors to make use of such devices when it comes to teaching subjects within the four confines of the classroom. Over a span of ten years, the models comprising of the pedagogy model, the learner models, and the domain models have significantly reached their zenith and are now being used by a number of tutors in order to make sure that learning is undertaken in a much more comprehensive manner, primarily focusing on oneon-one education. An interesting example that can be cited here is the, iTalk2Learn System (iTalk2Learn) which was specifically made in order to act as a help to young individuals enabling them to understand fractions and other mathematical problems with ease. This system used or adhered to a learner model and contained information or stored information which specifically dealt with the

2020 Handbook on Artificial Intelligence and International Law

information pertaining to an individual learner’s mathematical skills, their cognitive knowledge and how their emotional state was at the time when they were dealing or learning mathematics. Model based tutoring software’s comprise of a large number of AIED tools. For instance, ─ model learners have the cognitive ability and an affective state (Grawemeyer, et al., 2015); ─ the ability to understand a student’s perspective and enabling the student to engage in a Socratic Learning experience, which primarily involves dealing with experiences that take into consideration enquiry, discussions, questioning and finding the answers to those questions (Language processing in AIEd: Successes and challenges. Presented at the Panel on the Evolution of AIEd @ AIEd09, 2009); ─ taking into consideration an open learner model which has the ability to understand and deal with the various aspects pertaining to reflection and self-awareness (Preface: “Open Learner Models: Future Research Directions", 2007); ─ Adhering to meta-cognitive scaffolding (for instance, analyzing and understanding dynamic help or adhering to a narrative framework) when it comes to motivating and increasing the engagement of an individual student (Du Boulay, et al., 2007 pp. 563-565); and ─ understanding and making use of a social model, which can be simulated - for instance, in order to ensure that speakers have the capacity to properly speak and understand a language, it is imperative that they engage and connect successfully with speakers who speak in the language which an individual wishes to learn, thereby understanding and getting a hang of their cultural and social norms (Johnson, et al., 2009 p. 72);

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Section 2: AI & International Affairs

2020 Handbook on Artificial Intelligence and International Law

Basics of International Law

Abhivardhan & Mridutpal Bhattacharya

Introduction The chapter is dedicated to provide a crisp introduction to the basics of international law. A deeper context of international law has been reasonably dealt in the chapter on International Law & Diplomacy. Additional context is available in the chapter on International Human Rights Law.

Legal Background Sources of International Law

The International Court of Justice, has identified one of the primary sources of International Law in the International Court of Justice Statute, the 38th Article which reads as: 1. “[T]he Court, whose function is to decide in accordance with international law such disputes as are submitted to it, shall apply: a.

International conventions, whether general or particular, establishing rules expressly recognized by the contesting states; b. International custom, as evidence of a general practice accepted as law; c. The general principles of law recognized by civilized nations; d. Subject to the provisions of Article 59, judicial decisions and the teachings of the most highly qualified publicists of the various nations, as subsidiary means for the determination of rules of law. 2. This provision shall not prejudice the power of the Court to decide a case ex aequo et bono, if the parties agree thereto.” (UC Hastings, 2021) 3. Apart from the aforementioned article, he primary sources of International law are globally accepted to be – ─ ─ ─ ─

Treaties; Customary International Law; Principles of International Law; Writings of Publicists;

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─ Judicial Decisions; ─ Non-Legally Binding Instruments. Basing upon the global views of varying jurists, it is widely accepted that there are three important theories which actually form the basis of International Law. These theories, have been classed as Schools of International Law.

Naturalist Theory. In the ancient times such as the 17th & 18th centuries, the influence of theology & the “law of nature,” caused the sciences & studies of international law to be dominated by the naturalist school. Naturalist theory proposed that besides the natural law known as jus naturae, there cannot be space for any other law. It was believed that International law & other systems of law are all subparts of the law of nature, this School maintained its beliefs that the validity of international law is based upon the Will of God & that sovereigns are subject only to divine law – the law of nature, devised by God. It was assumed that there existed a system of law which emanated from God or morals or reasons. Early writers could be called “naturalists” inclusive of the well-known Spanish theologians & jurists, Francisco de Vitoria who lived from 1486 to 1546, & Francisco Suarez who lived from 1548 to 1617. The German jurist, Sammuel Pufendorf who lived from 1632 to 1694 had denied the existence of any such positive rules, arguing that only natural law has in it legally binding norms. Pufendorf & the followers of his not only considered the basis of international law as the law of nature, but completely identified the two as the same as well. Dutch writer Hugo Grotius who lived from 1583 to 1645, was believed to be the “founder”/ “father” of the Law of Nations, believed natural law to be “a dominant element”. His work has been considered as authority to Naturalists & Positivists. The works of his brought in a lot of followers who were termed “Grotians”, who sought the refining of the concept of natural law (More, 2020). Criticisms. There exist numerous criticisms of the doctrine of natural law. • Authors in the 19th Century like Schwazenberger or Brown noted the propositions of the naturalist school to be vague enough to become “practically meaningless”, The meanings of the law of nature are not precisely clear & therefore there are possibilities of conflict in the interpretation of laws of nature (More, 2020). • Through the denial of the existence of the rules of positive law, extreme naturalists advocated a doctrine that is viewed by numerous modern scholars as not being supported by reality. The doctrine of natural law is reserved from the realities of international life & thereby lacks emphasis on over the

2020 Handbook on Artificial Intelligence and International Law

practice of relations between States. Therefore, naturalism includes its disconnection with reality (More, 2020). • According to Kelsen, the duties & rights presume the existence of a legal system, which might only be established through acts of men or associations of men. The system naturalists are always in denial of (More, 2020). • The Law of Nature has always had a pivotal role in the development of International Law when the world was stationary. The times of today are dynamic & hence, the laws of nature are inadequate & unprogressive (More, 2020).

Positivists Theory. This theory is based upon the following premises: • The State is metaphysical in reality, & has a value & significance of its own (More, 2020). • It has its own Will (More, 2020). • The State’s Will possesses complete sovereignty & authority (More, 2020). As per the positivist school of thought the growth of International Law has been influenced by treaties & customs, instead of human nature, reason, & justice. Positivism basically teaches that the law of nations happens to be the aggregate of positive rules by which the States have decided to consent to be being bound, irrespective of any concepts of natural laws like ‘reason’ or ‘justice’. In relation to the positivists, nothing is to be known as ‘law’ among States which are not consented to by them. As per this theory, the positivists believe the will of the States to be absolutely sovereign & that, precisely is the source of the validity of all laws. The positivists as per Starke observed that the rules pertaining to international law, in the end, are similar to the domestic law as they both derive their binding force from the Will of the State. Thus, the only principles that might be deemed by law are as per them, the ones adopted with the valid consent of the States (More, 2020). Alberico Gentilis (1552 – 1608) & Richard Zouche (1590 – 1660) are said to be the originators of the school of positive law. Bynkershoek (1673 – 1743) – one of the leaders of the school, emphasized over the “principles of bona fides” as the theoretical foundation of all agreements amongst States. In his famous Quastionum juris publici, he placed emphasis upon the importance of the practice of modern Treaties, States, & Customs, completely ignored “Law of Nature” & ended up holding that: • The rules of international law were established through the consent of States; and

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• All agreements between States were the products of their sovereign wills (More, 2020); Criticisms.

• All rules of International law are not derived from Customs & Treaties, but some from the general principles of law that were recognized by civilized nations (More, 2020). • The presupposition of a State being a metaphysical reality & possessing its own will is purely metamorphic, it is by no means the will of the State by the will of the individuals who form the State (More, 2020). • A State always remains bonded by rules of International Law even when it has not given it valid consent. Practically, the validity of International Law is independent of the consent or assent of a particular State, however, it depends upon the principle that it is generally recognized as the society of the States (More, 2020). • States sometimes, are bound by General International Law even when pitted in opposition to their will. Any multilateral treaty adopted to by an international conference might be binding upon all the members partaking in the conference, notwithstanding the validity of their consent, contingent upon sufficient majority votes for the resolution & it is further ratified by a minimum number of States (More, 2020).

Eclectic or Grotian Theory. Apart from the classical naturalists, & positivists is the third school of the eclecticists or the “Grotians” who, alike Grotius, aimed at harmonizing the extreme positions of naturalism & positivism. Irrespective of being eclectic, the supporters of eclecticism were more or less naturalist or more or less positivist, despite most of them actually being naturalism-oriented. Wolff & Vattel essentially belonged to the naturalist school, they were Representative eclecticist jurists. They accepted the simultaneous actuality of two tiers of law – the natural level & the positivist level. Therefore, according to this theory – International Law has derived from natural law & consent law. Consent happens to be the basis, however not the only basis of international law (More, 2020).

2020 Handbook on Artificial Intelligence and International Law

Role of Political Ideologies and Concepts Nationalism. • Nationalism aims towards the preservation & fostering of a nation’s traditional cultural revivals. While cultural revival has been associated with nationalist movements. It encourages pride in the national achievements & is intricately linked to patriotism;

Globalism. • Globalism, fundamentally is sought for description & explanation of a world which characterizes itself via networks of connections that range multicontinental distances. It attempts at understanding all the inter-connections of the modern world - & at highlighting the patterns that underlie while explaining them;

Ethnonationalism. • Ethnonationalism is fundamentally, a kind of nationalism in which the nation & nationality are defined through terms of ethnicity. While emphasizing upon ethnicity centered approaches towards various political issues that are related with national affirmation of particular ethnic group(s). The approach & theme is that the nations are defined via shared heritage, which usually includes common language(s), common faith(s), common ethnic ancestries, etc., this includes ideas of culture that are shared amongst members of the group & their ancestors, thereby causing unity & solidarity;

Cultural Pluralism. • Cultural Pluralism calls for a group to not only coexist side by side, but to as well consider the qualities of other groups as traits which are worth possessing in the dominant culture. Pluralistic societies place immense expectations pertaining to integration on members, rather than exhibiting expectations of assimilation;

Cosmopolitanism. • Cosmopolitanism, fundamentally is a ‘moral’ commitment towards aiding humanity as such, much contemporary moral philosophy places insistence upon duties such as the duty to aid foreigners who might be starving or suffering, or at least the duty towards respect & promotion of basic human rights & justice;

Anthropocentrism.

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• Anthropocentrism considers humans to be separate from & actually superior to the nature & further holds that human life happens to be intrinsic value whereas other entities (inclusive of animals, plants, mineral resources & so on) are resources that might be justifiably exploited for humankinds’ benefit;

Ecocentrism. • The importance of ecocentrism is due to many reasons, it expands the moral community & from being just about ourselves. It therefore means – we are not concerned with humanity only, but we are to extend respect & care to all life & indeed to terrestrial & aquatic eco-systems;

Regionalism. • Advocates of regionalism maintain the view that strengthening the authorities & political powers within a particular region while maintaining a central government, & shall as well benefit local populations via improvement of regional/local economies;

Subsidiarity. • Subsidiarity is fundamentally, an organizing principle that matters should generally be attained to by the lowest or smallest or least centralized competent authorities, based on the belief that political decisions should be made at the local level if possible, rather than by a central authority;

Individualism. • The primary belief in this case is that the human individual is of the utmost importance with regards to the struggle for liberation, & thereby the promotion of individuality is the aim, as the name suggests;

Collectivism. • The primary outlook is the consideration of individuals as societal structures as whole in terms of international law;

Orientalism. • The primary aim is the maintenance of the asymmetry in the relationship of power among the West & the Orient as well as the apparently unavoidable incompatibility of the respective cultures & civilizations;

2020 Handbook on Artificial Intelligence and International Law

Science & Technology Vis-à-vis International Law In the later phases of the Second World War, the inception of a much fearsome agent of mas destruction was cradled. The initial reaction of the scholars towards the hecatomb caused at Hiroshima & Nagasaki owing to the atomic bombings – was that, International law needs to inhibit the use of such a destructive force in the future. However, such an attitude, which is understandable, is not practicable. The States that control such weaponry would hate to relinquish their advantages over potential enemies & thereby the enemy States would agree to negotiation over disputes only when they fear the usage of such weaponry against them (Nascimento e Silva, 1984). Later on, the technology used for making these bombs gave way to the inception of the first ever nuclear power plant. Again, International Law had to intervene into this newfound field & therefore, the International Atomic Energy Agency Statute was framed & consequently signed in 1956 at Vienna. It laid down two fundamental purposes – to cause the acceleration & enlargement of the contribution of atomic energy for purposes of peace, & to ensure that the nuclear energy is not utilized in ways that might further militarily needs. The Statute provided to the Agency the power to establish safeguards for worldwide disarmament as well. The Non-Proliferation of Nuclear Weapons Treaty signed in the year 1968, happened to herald a major contribution to World Peace, & was in sort of a step backwards – as it legalized the proliferation of nuclear armaments by the nuclear weapon States whereas also created additional safeguards for the States that are interested in pacific needs only with regards to nuclear energy (Nascimento e Silva, 1984). The unexpected & vivid effect that nuclear energy caused in numerous spheres of human life inclusive of the field of International law needs no validation. Therefore, study of the impact of science & technology with regards to the field of International Law is imperative. With nuclear energy in the background, & cyber warfare slowly but steadily coming into the foreground, the next big thing is Artificial Intelligence. AI is to be the next atomic bomb, & it needs to be treated as such, in simpler words as opposed to that allegory, a malevolent AI or an AI in the hands of malevolent individuals & organizations might as well be able to hack into the international cyber systems in order to unleash nuclear destruction on other States to cause a war. It might be a radical organization that causes this, it might be a weapons dealer that causes this for his own profit, whatever it may be, safeguards need to be put into place, & for that studies need to be done effectively & extensively (Nascimento e Silva, 1984).

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Conjunction with AI The conjunction of pure international law with the principles of AI Ethics has to be understood from some important angles of references. Some of the angles of reference that international law bears in the case of including AI as a subject matter in the same field, are enumerated as follows: 1. Entitative Status of AI under legal recognition, or juristic interpretation 2. Treatment of AI as State or Non-State Actors 3. The Effect of Disruptive Technologies in affecting the understanding of International Law 4. Algorithm and International Law Background In generic conjunction, it is important to understand that Artificial Intelligence can be understood as (a) a concept; (b) an entity; or even an (c) industry. As a concept, AI contributes in developing the field of international technology law prominently, considering the integral nature of the concept with the field of technology sciences. We also know that scholarly research is in course with regards to acknowledging and ascertaining how AI is relatable and connected to fields like international intellectual property law, international privacy law, international human rights law & international cyber law. Thus, as a concept, it is clear to infer that AI has to be accepted in the best possible ways, which serves better checks and balances, and concept of jurisdiction, whether international or transnational, is suitably established and encouraged. As an entity, questions have been largely on the entitative status of AI. Since, AI is an abstract concept, as an entity, autonomous vehicles, robots, facial recognition systems, etc., are within the practical and tangible categories of what constitutes an AI. Some laws and regulations mention the term not as AI but as algorithmic systems, autonomous systems, automated systems and so on. We would be using the term ‘AI’ in a loose fashion throughout this book, but with a purpose that the chapters serve a reasonable cause in teaching and explaining how AI is recognized legally. On the question of the entitative status of AI, under jurisprudence, there can be 2 distinctions on a prima facie basis: (1) the legal status; and (2) the juristic status. The former relates to the idea that a competent authority, i.e., either a state (through its legislative wing or the executive wing) or a treaty body/intergovernmental organization can effectively draft a law (regulation, treaty, declaration, covenant, constitutional amendment, rule etc.,). Obviously, in the case of an international organization, the process differs as accession to ratify/signature to ratify is something that countries have to decide. At national jurisdictions, governments can enact laws

2020 Handbook on Artificial Intelligence and International Law

on recognizing the status of AI as well, which effectively forms an important trend or part of the state practice recognized under international law. The latter status is quite different. It is not that governments or international organizations cannot have a say here, but the recognition here would-be fluid and interpretive. Most of the time, it is the quasi-judicial bodies, expert groups, standing committees and even the courts, which decide the juristic status of something. Traditionally, here in the case of AI, this would be an appropriate method, but reckoning it is not exactly as legalistic as in the former case it would be. AI as an Industry is a tricky territory, which has to nurture and evolve with the changing times. Now, it is the state primarily, which decides how it develops and maintains its economic and social interests. AI’s usage at an industrial level is imminent, considering the fact that it has its own importance in both the hard power and soft power aspects of the state. In the domain of hard power, AI can be used for cutting-edge defence tech, surveillance etc., while in the domain of soft power, AI can be used for public censorship, biometric and facial recognition, etc. All the three understandings will be discussed in the forthcoming sections of the chapter. Entitative Status of AI under legal recognition, or juristic interpretation

As discussed above, the entitative status of AI in law, strictly is understandable in one of the either ways, which are: • AI as a Legal Entity • AI as a Juristic Entity In both the cases, it is suitable to establish the substantive attributes of AI both as legal and juristic entities. There can be disagreements on the procedural attributes here due to the simple reasons that there at procedural levels, it is not practically possible to have similar legal standards of different kinds of products and services which involve AI directly or indirectly. For example, an automotive car is incomparable to a facial recognition software available on a smart phone. As discussed, we will be discussing AI as an entity, an industry & even as a concept wherever the existing literature supports.

Common Attributes. 1. We need to treat AI’s omnipotence and omnipresence whether as a legal entity or as a juristic entity. Omnipotence generally refers to the all-

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comprehensive and expansive abilities of an entity. Omnipresence, on the other hand refers to the all-comprehensive and expansive presence of an entity. So, when we discuss AI’s omnipotence – it has to do with the fact that business leaders and tech giants generally support the notion that AI should be all-invincible to avoid any risks in its activities and operations (Arbitral.com; Stewart, 1993). AI’s omnipresence therefore is about the test of outreach, influence & the constructive precautions behind the wide reach and utility of the AI-based product or service, which includes its ramifications. ─ As a concept, countries might have different scholarly views on AI’s omnipresence and omnipotence. However, based on juristic interpretation, it has to be seen effectively how such practices are recognized as state practice, which might ought to be reckoned as a constituent element of international legal custom; ─ As an entity, a focus would be more on the legal status of AI, but the vicarious effect of actions and operations led by or through AI-based products and services would define the cardinal aspect of corporate liability, since on a dominant basis, the principle of agency will be of utmost focus here; ─ As an industry, there can be some principled agreements among countries on the principles regarding human rights and liberties & how AI’s omnipresence and omnipotence can influence it, but even if that is possible, there should be more anthropological focus on how industries drive narratives and research on this aspect of AI Ethics, because here, the role and accountability of private actors, startups etc., would be of utmost importance. Sovereignty also would be importantly defined; 2. The economic and social utility of AI will always be put under question, because more or less, it would be a policy question, therefore reserving the matter to the executive and parliamentary branch of the governments. Additionally, intergovernmental organizations and expert groups should have a say, but only for suggestive reasons. Consultative recognition of the economic and developmental angle of AI’s recognition and agency would differ in the three categories as follows: ─ As a concept, focus should be more on the development and practice of the field, which is academically factual and non-partisan & constructive. Intellectual harmony in the schools of thought always invites better contribution in the literature of international law and artificial intelligence. The factor that AI has different utilities and presence depending on the kind of product/services involved also affects the scholarly position;

2020 Handbook on Artificial Intelligence and International Law

─ As an entity, the direct and indirect effects of actions and operations of AI would be put into question, and the principle of agency will be an important focal point for the purpose of adjudication and risk assessment; ─ As an industry, the economic and social utility of AI has to be in consensus with the three factors: (1) state consequentialism or state interests; (2) industrial motives and interests; and (3) the explanability and reasonability behind the industrial products and services central or related to AI; 3. Auditing of AI as a concept is as cogent and clearly needed among countries, because it enables companies and governments to measure and safeguard recognition of risk assessment and operations led through AI. Having international standards for auditing AI as an entity again would invite a new uncharted territory of question: How can the liabilities and responsibilities of AI as a product or a service or a reckoned entity (system) can be audited at an international level. Industry-wise auditing also is legally reasonable at subsidiary levels, and can be uniform at substantive levels; 4. The many hands principle (Council of Europe, 2018 pp. 62-64; Bjola, 2020) or the many hands problem also would be important for assessment in defining how state practices take into accord the notions of liability and accountability at national jurisdictions, which can then be accepted at international levels. The many hands principle would surely be a strategic uptake depending from country to country, when it comes to AI as a concept. As an entity, better juridical governance of courts and tribunals would be important to assess and ensure auditing the aspects behind many hands problem itself. As an industry, in both the cases, the many hands problem will invite intersectional policy making, which again would invite reasonable judicial intervention to assess how the status quo of the phenomenon has transformed and changed; 5. AI as a concept is abstract, with some commonalities at substantive levels. However, it is suggested that in terms of ascertaining international recognition, it must be kept in mind that the genealogical utility of AI as an entity, whether legal or juristic, would depend on the kind of product/service, thereby meaning that AI can have its own animal kingdomalike categorization, which can be model-centric and subject-centric (A Misdirected Principle with a Catch: Explicability for AI, 2019 pp. 495–514). As an industry, the intersectional behaviour of policy dichotomies and consensuses between the state and non-state actors (government and industry leaders) would be essential to see how the modalities develop and affect state practice and intergovernmental cooperation in multilateral organizations in matters related to policy intervention and rule of law;

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Specific Attributes – Legal Recognition. 1. As a legal entity, the genealogy behind the concept of rights, liabilities and duties changes. The concept of agency of AI as a product/service is the current practical means to assure corporate liability, but the anthropological basis of absolving from agency-based liability would radically differ based on the kind of AI being put into use. AI can be a subject, an object or a third party. In the first two roles, there are already established standards on civil liability, IPR, labour rights, employment, conflict law, etc., (European Parliament, 2020; European Parliament, 2017; THE BELL, 2018; Swiss Confederation, 2018 p. 2) in various countries, where the categories start from robotics to social media regulation and so on. The notion of third-party liability would be contestable when AI is recognized as a legal entity, but the subject-object recognition of AI as a legal entity in any form possible would invite more enquiry and introspection into the third-party status of AI for sure; 2. A Legal Entity has a defined and clear status under law. Exceptions like Sophia (Harvard Law Petrie-Flom Center, 2017) do not stand any generalization under the current law, which is anyways human-focused. The European Parliament’s work on AI and civil liability explicitly recognizes the need of an electronic legal personality (European Parliament, 2020), which also invites discussion on how the notion of liabilities and responsibilities could be decided; 3. International Law guarantees explicit liberties and rights to human individuals (also known as natural persons in EU Law for example), which starts from the 1948 Universal Declaration of Human Rights to the 1966 Covenants. Based on the specific nature of issue, international law has been shaped. However, in the case of AI, it is important to understand that the notion of AI rights can be achieved when the pattern of recognition and the personification is certain. The existing jurisprudence relies on a human-alike personification, which again problematizes the issue. First, human aesthetics and experience are inalienable, and must be included as the inherent right to privacy as well as the inalienable freedom of speech & expression. Sometimes, the intersectionality of human rights also leads judges to interpret the context of privacy and freedom of speech & expression as the inalienable aspects of the right to life of a human individual, which also must be taken into consideration, when the establishment and extension of ‘AI rights’ would be assured. The intersectional behaviour of the policy behind human rights and AI has been discussed in the forthcoming chapters of the handbook;

2020 Handbook on Artificial Intelligence and International Law

Specific Attributes – Juristic Interpretation. 1. Defining the rights, liabilities and accountabilities of AI as a juristic entity is a developing concept. Mostly, juristic interpretations can be used by courts, expert groups, standing committees and others to ensure short-term interpretations on the nuances and niche aspects of AI. 2. There is no absolute recognition given here, because it would depend also on the administrative regulation of the systems that are contributing to such an interpretation. For example, a common law judicial system, which relies on the concept of stare decisis, will be polyvocal, which means its estimation of the juristic abilities and restraints of AI will be subject to interpretation. If a matter of judicial review comes in, then maybe, the limited personification rendered would be also tested to ensure that the status granted is constitutionally legitimate. However, the nuances would then be too delicate and substantive. At procedural levels, the polyvocality in a common law system would surely affect the validity, reasonability, extent and permissibility of the juristic status or persona granted to the AI; 3. Polyvocality generally means that in a common law judiciary, the courts would generally interpret any subject-matter of law in such a manner that under the principle of ratio-obiter, the court might render differing obiter dicta, while even interpreting the principle behind the judgment/order rendered, the court can alter some aspects of the substantive principles behind (which through judicial review can be subjected to policy/legal intervention by the court). Generally, polyvocality is possible in common law judicial systems, but still, it has its value at the level of international bodies such as the ICSID, the International Criminal Court, the International Court of Justice, the WTO Appellate Body and others; 4. The volatile nature of the industries, for e.g., robotics, automation, recognition software, augmentation, UI/UX, consumer experience etc., will also influence the procedural attributes of the nature of interpretation of courts, and so the polyvocality assumed by the judges in matters, based on the kinds of jurisdictions recognized; Treatment of AI as State or Non-State Actors Treating AI as State Actors or Non-State Actors would also be an important question, in the field of international law, and the annals of recognition might even diversify with the specificity of the fields, which are put into adjudication. However, there are some common understandings which would revolve around the question of treatment:

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• The notion of sovereignty recognized under customary international law & international law recognized by intergovernmental organizations would also be decently affected. The reason is that the interpretation of rendering liability or no liability would anyways be influenced with the practical conditions, thereby affecting the principle of sovereignty; • The question of jurisdiction (international or national) would also be asked, when adjudication would happen among multilateral bodies or governments or companies so to mention (for example in private international law); • Treating state actors and non-state actors would also change at the secondorder levels of international law fields. For example, in general international law, there can be some consensual understandings of what constitutes a state actor or not. However, depending on the kind of international law field that would be subjected to review based on the subject-matter of the dispute, the way state/non-state actors are treated would inherently differ;

AI as a State Actor. Sovereignty has its own benefits and means. The sovereignty of a state defines the immunities, liabilities and privileges earned by the government and the officials, who are affiliated with the state and protected under international law. When we include AI as a state actor, it should be asked as how much administrative accountability and transparency we must expect from AI as a state actor. The reason is that unlike other state actors, where human involvement is based on the sui generis notions of control and action, AI being a state actor involves recognition of national legal or juristic recognition of AI and its system of rights, liabilities and responsibilities. Some aspects of AI being a state actor, which can be taken into consideration for review other than the basic principles of rule of law, fairness, constitutional morality and transparency can be: • The participatory role and the relative degree of policy/administrative intervention (cum participation) subjected of the AI realm; • Transparent explicability of the AI system (Explainable Artificial Intelligence); • The role of AI as an agent of the state, and the degree of foresight; • Subjective personification of AI as a State Actor; • Comprehension and implementation of the commitments and duties as per the TIID Framework (for example) (Bjola, 2020);

AI as a Non-State Actor. As a non-state actor, the role of AI shifts because there can be more dynamic

2020 Handbook on Artificial Intelligence and International Law

realms of the legal/juristic personality earned or recognized by a competent authority. Recognition by international organizations is subject to their real conditions, anyways. In the forthcoming case studies, the characteristics of AI as a non-state actor will be emphasized upon. Algorithm and International Law Algorithm is a fundamental unit or constituent of activity of the machine learning configuration/artificial intelligence system, and the relationship between the data/information learned and the algorithm(s) put into use creates immense possibilities to develop the influence of algorithmic activities or operations in the field of international law. Generally, algorithmic operations and activities in the economic, diplomatic, social, individual and administrative domains of the state and its people have been recognized by various developed and developing economies. Algorithms are generally criticized and analysed under some common principles of AI Ethics, and so are considered an important element in the field of AI Ethics. In international law, the role of algorithms is central to diplomatic activities and has also to do with respecting the sovereign character of the coordinated principles of global administrative law. In the case studies for this chapter, the authors will discuss the role of algorithmic operations and activities in international law.

Case Studies European Parliament Report on AI and Civil Liability, 2020 As requested by the JURI Committee, the Policy Department for Citizens' Rights and Constitutional Affairs in the European Parliament published the work authored by Andrea Bertolini in July 2020 (European Parliament, 2020). These are the following features and findings of the work published by the EU: • AI-based applications and machines are products and current there exists no technical, legal or philosophical considerations that adequately justify its own traits to be recognized under the emblem of a legal personality, i.e., autonomy, machine learning and its dynamics, modifiability & technical fungibility in achieving ends; • There is no responsibility gap, which could be clearly identified. The agency rule works here;

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• Legal personification is fictional interpretation in jurisprudence, which can limit liabilities, replaces contracts and also can differentiate tax treatment among different persons; • Ontologically, there is no machine, whose electronic legal personality recognized by law will be vested with the rights and duties that can be equivalent to those of human beings as natural persons under European Law (European Parliament, 2020 p. 36); • Some autonomy and its nature would decide how the electronic legal persona can be encouraged. However, the current notion is that is no need to grant legal personhood to emerging digital technologies (European Parliament, 2020 pp. 34-36); • There are 2 aspects of interpretation of AI as an electronic legal personality, where the author recognizes that an alternative interpretation would emphasize on a functional corollary of AI as a legal personality (electronic), which might not have any abstract rights and autonomy features, like duties and responsibilities, but the determination of liabilities would become more distinct; • It is not sensible to impose moral responsibility on AI, because its intentionality to act is not independent or dynamically autonomous like human entities, or human-administered legal entities, such as trusts, companies, governments etc.; • The functional dimension of AI/machine as an electronic legal personality can be determined from corporate law and policy developments; • The Class-of-Applications-by-Class-of-Application (CbC) approach (European Parliament, 2020 pp. 41-43) emphasizes upon a case-by-case basis of review and risk assessment under corporate policy to assure the determination of the electronic legal persona of the machine/AI; House of Commons Science and Technology Committee, Algorithms in Decision-Making (HC 351, May 2018) • The Committee in this report specifically emphasizes on enquiring the concept of algorithm. They recognize the usual industrial relationship between big data, computation and algorithmic activities/operations (House of Commons Science and Technology Committee, 2018); • They lay their specific emphasis on various sectors such as healthcare, criminal justice, social media and web & data sharing governance; • The work emphasizes the issue of algorithm and data-related biases and proposes preliminary focus on ensuring compliance mechanisms;

2020 Handbook on Artificial Intelligence and International Law

International Law, Diplomacy & Dispute Settlement

Mridutpal Bhattacharya

Introduction Origin, Purpose, Nature & Etymology of Diplomacy Diplomacy refers to management of relations & mitigation of conflicts among nations (Oxford Learner's Dictionary) as per the basic dictionary meaning of the world, provided in the Oxford Learner’s Dictionary, while a person who represents his or her country in such diplomatic activity is referred to as a “diplomat”. The origin of the word “Diplomacy”, trace back to ancient France, the etymological root of the word is the Latin word “diplōma “, composed of “diplo” meaning “folded in two” & “ma” meaning “an object”. The word basically referred to a folded piece of document that the princes of old used to hand out to the citizenry as means of granting favors – often consisting of a permit to travel. Later the word was used to refer to all such documents that were handed out by chancelleries with regards to sovereign states. A prevalent misconception is that Diplomacy is more often than naught concerned with foreign policy, the former does deal with the latter at times but the terms are not synonymous. The vast distinctness between the two is that Foreign Policy – has a design for the establishment of goals, prescription of targets, & sets up tactics for usage towards achieving accomplishments. While, Diplomacy – is the principal substitute for the usage of force or underhanded techniques to achieve an objective, it’s how amicable relationships are maintained between sovereigns by mutual compromises & adjustments. Diplomatic maneuvers might at time seem to be of a coercive nature but are overtly non-violent. The primary tools of the trade with reference to diplomacy are, dialogue & negotiation – primarily conducted by designated envoys or liaisons. The purpose of diplomacy is to further the interests of a State – derived from geography, history, economics, & the distribution of international power. Other interests that are borne in mind throughout diplomatic procedures are safeguarding of national independence, security, & integrity – territorial, political, economic, & moral. These are viewed as the primary obligations of a country. All policies are designed by policy makers & legislators in pursuance

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of their perception of national interest, while adjusting national policies to changes in external conditions & technologies. The leadership staff of a nation presides over the foreign ministry, & direct policy execution, supervise the ministry’s officials & instruct the country’s diplomats abroad. History of Diplomacy The prevalent belief in Europe of the late medieval period was that the original diplomats were angels descended to Earth with purpose of advocating peace; this belief was perhaps propagated owing to the fact that some facets of diplomatic machinery predate recorded history itself. The medieval societies had the concept of States to a certain extent & the first instances of something alike international laws were exhibited in certain intertribal relations, revolving around negotiated marriages, regulations on hunting & trade, etc. In the medieval context, women were seen as entities best suited for the bringing forth of peace owing to their inherent sanctity & usage of “sexual wiles”. There are traces of diplomatic activities & other instances dating back to the 14th century BCE, but none of those traces have been found in western Africa before the 9th Century BCE. The inscriptions on Mayan city walls indicate that exchange of diplomatic envoys were prevalent & frequent though nothing specific or details in are known. The dispatch of envoys in South America however appears to be a prelude to conquest rather than endeavors towards bargaining between sovereigns. A diplomatic communication persisted between the Egyptian Court & a Hittite king on cuneiform tables in Akkadian which wasn’t the native language of either of the parties. The oldest surviving evidence of treaties in the form of full texts surviving, are from about 1280 BCE, between Ramses – II of Egypt & Hittite leaders. There are important references to Assyrian Diplomacy of the 7th Century – principally in the Bible & that of the relations of Jewish tribes with each other & other people. The first records of Chinese & Indian diplomacy date back to the 1st millennium BCE. By the 8th Century BCE, the Chinese had formed leagues, missions & other such organized systems of well-mannered addresses between their many “warring states”, these formations included residency to envoys who served as hostages to the good behaviors of those who sent them. The virtues of such endeavors & principled behaviors stood in emphasis upon the sophistication of the tradition. These formations are well documented in the Chinese Classics, the essence of which was best captured by the advices of Zhuangzi to “diplomats” at the beginning of the 3rd Century BCE. These traditions ceased when the country came under the Qin emperor in 221 BCE & with the consolidation of unity under the Han dynasty in 206 BCE. Along with every new succeeding dynasty, the foreign relations were limited more & more to the

2020 Handbook on Artificial Intelligence and International Law

extent of only the defense of China’s borders. It was much further in the future that European Colonialism overwhelmed the Chinese dynasties & the Chinese governance & introduced Asia to the European concepts of Sovereignty, suzerainty, spheres of influence, & other diplomatic practices, traditions & norms. Ancient India was residence to an equally sophisticated but vastly different diplomatic framework & traditions. The traditions were systemized & described in the Artha-Shastra by Kautilya, who had once helped the young Chandragupta to overthrow Macedoniann rule in Northern India & guided him towards the establishment of the Mauryan Dynasty at the end of 4th Century BCE. The dynasty suggested that foreign relations should be determined by self-interest rather than by ethical considerations. It categorized state power through twelve facets & laid emphasis upon espionage, diplomatic maneuvers, etc., within a complex geopolitical matrix. Conciliation, seduction, subversion, coercion these four methods of statecraft & six forms of state policy – peace, war, nonalignment, alliances, shows of force, & double-dealing these six forms of state policy also presented. In order to execute these thoughts & methods, ancient India employed three categories of diplomats – plenipotentiaries, envoys used for a single issue or mission, & royal messengers & demanded two classes of spies – those charged with the collection of intelligence & those entrusted with subversion & other forms of covert actions. Kautilya had further demanded that royal messengers were not to be harmed even if they were bearers of unpleasant news. The regions that constricted the boundaries of operations for these constructs were separated from the neighbors by deserts, seas & the Himalayas. India had very little political contact with the rest of the world before the conquering of the northern provinces of India by Alexander the Great in 326 BCE. The subsequent foundation of the Mauryan dynasty brought forth a new era in Indian Diplomatic History that was marked by the efforts made by ancient Indians to spread the native doctrines & religions such as Buddhism. The Mauryan ruler Ashoka – was particularly active in diplomatic missions & deployed a lot of Brahmin – led missions to further contacts with the world. These activities were ceased in during the reign of the Rajputs in 8th to 13th century BCE. India was once again isolated from the whole wide world. The Chola dynasty & other Dravidian kingdoms of South India continued diplomatic activities& exchanges & contacts with South-East Asia & China were frequent. China preserved the text & memories of the Artha-Shastra, which was forgotten & replaced by the ideals & systems of the Mughal & British conquerors. The inception of the modern world system of international relations & postrenaissance European diplomacy cradled in ancient Greece, the earliest demonstrations of Greek diplomacy can be found in the literature. Remarkably in Homer’s Iliad & Odyssey, apart from that, the first traces of inter-national

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relationships were exhibited by the Olympic Games of 776 BCE. The Amphitryonic leagues maintained inter-national co-operations & collaborations with extraterritorial rights & permanent secretariats. Sparta actively formed alliances in the mid- 6th Century BCE, & by 500 BCE it had formed the Peloponnesian League. In the 5th Century BCE, Athens led the Delian League during the Greco-Persian War. References to Heralds can be found in ancient Greek literature. They were the first diplomats & were protected metaphorically protected by the gods – they had certain immunities that envoys had originally lacked. Their supposed protector was said to be Hermes – the messenger of Gods, the herald of Zeus. Hermes as a god, was noted for persuasiveness & eloquence but on the other hand was also known for knavery, deceptiveness, & dishonesty – which caused a certain prejudice to diplomacy which the modern diplomats still try to disapprove (Marks). The heralds were inviolable & therefore were the preferred means of communication during war-time. The preceded envoys to ensure fair & safe passage, & travelled alone. The envoys usually travelled in small groups to ensure loyalty & trust & were generally more than fifty years old & were political figures in their respective communities. Their oratorical skills were tested before they were deployed in the field. Greek diplomacy however was episodic instead of continuous. Unlike the ambassadors of today, Greek heralds resided in a city for short spans of time in seeking influence over the citizenry & the governors of the city. To further the object of trade, proxeni were deployed, they were residents of a city while the targeted city employed them, their task was primarily to further trade & commerce relations & secondarily to gather information. In Herodotus’s History, he indicated that Greek consuls operated in Egypt in 550 BCE. The archives, diplomatic vocabularies, principles of international conduct that anticipated international law, & many other things on modern diplomacy were developed by the Greeks. Truces, neutrality, commercial conventions, conferences, treaties, & alliances were commonplace; even the smallest provinces had the right to be heard. Rome thrived on what the Greeks had devised & modified & adapted it to suit their needs for imperial administration; with the expansion of Rome, negotiations with representatives of conquered areas took place. Treaties were made with other provinces as per the Greek International Laws. The Roman Republican Senates conducted foreign policies, via a separate department for foreign affairs. Later on, the Emperor became the ultimate authority in foreign affairs. All envoys were received with ceremonies & magnificence & immunities were granted to them & their accompanied aids. Roman envoys were deployed with written instructions from their respective governments, sometimes in the form of messages, other time as nuntius sent to towns. For larger provinces, a legatio (embassy)of 10 to 12 envoys was deployed, led by a president – the Legati, who were leading citizens chosen for their skills in oratory & had inviolable

2020 Handbook on Artificial Intelligence and International Law

immunities. Rome also created sophisticated archives, which were staffed by well-trained archivists. Paleographic techniques were employed to decipher & authenticate ancient documents. Roman Law stressed upon the sanctity of contracts & became the basis of all treaties. Late in the Republican era, the laws in force in Rome that governed foreigners & the laws that governed the treatments towards envoys were merged with the Greek concept of natural law. The inviolability of treaties & the law of nations was later absorbed by the Roman Catholic Church & was preserved for centuries after the collapse of the Western Roman Empire. A foundation had thus already been built for increasingly sophisticated doctrines & models of international laws to be cradled. Thus, international laws began to surface along with the idea of the European nation-state a millennium later. In the Middle Ages, the disintegration of the Western Empire in the 5th Century CE, caused most of the diplomatic traditions to disappear. Nevertheless, while monarchs negotiated directly with approximate rulers & distant rulers through envoys from the 5th to 9th Century, the papacy continued to use the Legatio. Both diplomatic forms intensified in the next three centuries. The Eastern half of the Roman Empire functioned as the Byzantine Empire for nearly 1,000 years. The Byzantine Court at Constantinople, where envoys by the Papacy were sent in the 5th century, had a department of foreign affairs & a bureau to deal with the foreign envoys. The aims were to awe & intimidate the foreign envoys to submission. The arrivals of envoys were marked by spectacular ceremonies that were precisely calculated as shows of power that the empire possessed. The Renaissance Period was remarkable with reference to diplomacy; however, it remains unclear which Italian province first employed permanent envoys, in the late Middle Ages & early Renaissance period, most embassies were temporary, spanning three months to two years. In the late 14th & early 15th Centuries, Venice, Milan, & Mantua sent resident envoys to each other, to the popes & to the Holy Roman Emperors. At this time, envoys generally did not travel with their wives, but their missions usually employed cooks for the purposes of hospitality & to avoid the risks of being poisoned. Resident embassies turn out to be the norm in Italy in the late 15 th Century, & in the subsequent years the practices started spreading northward. Ambassadors served many roles, including the reporting of events to their government & negotiating with their hosts. In addition, they attained the role of commercial consuls. The earlier economic revivals, the geographic locations & small size fostered the creation of a European state system in miniature. The complete organization of the peninsula into states ascertained frequent wars & the maintenance of the equilibrium deemed necessary constant diplomatic constraints. While, conferences among the rulers were considered to be unobtrusive & risky, resident envoys served to be far safer & more effective

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means of mitigation. Consequently, the system of permanent agents took over, with members of the upper middle class or younger sons of great families serving as envoys. Rome became the center of Italian diplomacy, intrigue, information & spying. Popes entertained ambassadors but did not employ them. The Court of Papal had the first integrated diplomatic outfits: the popes addressed the envoys collectively, seated them as a group through the ceremonies, & constituted rules for their collective governance. With residency of envoys becoming the norm, ceremonial & social occasions became more dominant & further influence positively the diplomatic relations, all the partaker in the occasions had to show their hands & forces to ensure their sovereignty. Papal envoys started taking precedence over temporal rulers. Spain did not accept inferiority to France & power struggles ensued. Papal power declined, & the protestant revolts complicated matters further along with turmoil with respect to the Pope’s position. By the 16th Century, the title of ambassador was in usage only for envoys of crowned heads & the republic of Venice. Latin remained the international language of diplomacy. The French invasion in 1494 defied the Italian States with as intervention by a power greater than any within their own states. They were determined to replace diplomacy & convenient, perhaps short-lived, compromise for the force they lacked. This ideology coupled with their enthusiasm for diplomatic nuances & the 16th century writings of Niccolo Machiavelli, gave Italian diplomacy a reputation of being devious. But, it was no more devious than that of the other states, Machiavelli –a Florentine diplomat himself, argued that an envoy needed integrity, reliability, & honesty, along with the tact & skill in the use of occasional equivocation along with the usage of selective abridgment of aspects of the truth unfavorable to his cause – views seconded since by virtually every authority. The 16th century was waged in Italy, the emergence of headstrong states north of the Alps, & the Protestant revolts ended the Italian Renaissance but spread the Italian diplomacy far & wide. Henry VII of England adopted the Italian diplomatic system, & initially even used Italian envoys. Thomas Cardinal Wolsey, Henry VIII’s chancellor formed an English diplomatic service. Under Francis-I, France also adopted the Italian diplomacy system. The Law & the Lawyer in Diplomacy His Excellency, Wilhelm G. Grewe – German Ambassador at the American Society of International Law at Its Annual Meeting, delivered quite a popular monologue. He delved into the subject of a lawyer as a diplomat. Quite a few literary mentions were made by him & quite a few people were mentioned by him, their contentions for or against the prospect of lawyers as diplomats were talked upon. For instance, he mentioned Harold Nicholson - a professional writer on diplomacy for having written that

2020 Handbook on Artificial Intelligence and International Law

“The worst kind of diplomats are missionaries, fanatics and lawyers”. (Proceedings of the American Society of International Law at its Annual Meeting, 1960 pp. 232-237) While this was admittedly not much of a flattering contention, Charles Thayer was mentioned who had recently published a book Diplomat, which had a little softer tone on the association of lawyers & generals in the military with diplomacy. Thayer said, “Though generals and lawyers are frequently appointed to diplomatic posts, most authorities agree that neither group is well suited to diplomacy”. (Proceedings of the American Society of International Law at its Annual Meeting, 1960) In order to disqualify the military, he recalled the story of a friend of his who was in the sphere of diplomacy, who read in the papers of appointment of a series of generals & admirals to diplomatic posts, following which, he asked lightheartedly “Do you suppose that one day they will appoint Ambassador Smith to take command of the Fifth Fleet or Counsellor of Embassy Jones to head the 45th Jet Fighter Squadron?” (Proceedings of the American Society of International Law at its Annual Meeting, 1960) His Excellency’s intention was not to be involved in an argument on the military as diplomats. But the desired result was not achieved, as the author Charles Thayer happened to be his friend. With whom, having met for the first time at the American Embassy in Berlin, a conversation ensued, an excerpt from which is as follows: “Although it is commonly supposed that the legal career is an advantage for diplomats and though many lawyers have been appointed to embassies, law in some respects is even less suited as training for a diplomatic career than the military profession.” (Proceedings of the American Society of International Law at its Annual Meeting, 1960) Thus, the contention of his Excellency was that the lawyer cannot escape this verdict & in Germany the judgment would certainly not be more favorable in general. However, as example, he cited the fact that the first administrative chief of the German Foreign Service since 1951 and at the same time the first political advisor of the Chancellor in foreign affairs was a law professor with the name of Walter Hallstein, State Secretary at the time, & later the President of the European Economic Community. Nobody had previously been more criticized or attacked than him. The German Press criticized the man time & again as a doctrinaire & a legalist & a man blind to realities & fixed on theoretical ideas & issues. The point of attack was not only legal mind; it was at the same time the professional attitude that was attributed to him. It was however a strange phenomenon that the German polls regarded professors as holding the highest degree of social prestige (Proceedings of the American

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Society of International Law at its Annual Meeting, 1960). His Excellency quoted Thayer in saying that “The lawyer like the diplomat deals in debate and compromise. Knowledge of law is essential to the diplomat, an ability to negotiate is essential to the lawyer and a knowledge of human nature is essential to both.” (Proceedings of the American Society of International Law at its Annual Meeting, 1960) However, Thayer’s further contention was that these similarities are only superficial. When a lawyer actually turns to international problems, these similarities are considered to lead him towards a false conclusion that diplomacy is a form of law. Thayer argues – “His whole training has accustomed him to presuppose a court, where right is distinguished from wrong, legal from illegal, and where there are police and jails to enforce decisions. Moral as well as legal concepts govern his thinking.” (Proceedings of the American Society of International Law at its Annual Meeting, 1960) His Excellency however made a valid argument that this contention does not hold true for the teacher & student of international law. The understanding of international law begins with the clear insight to the imperfections of the international legal order, which has no complete system of sanctions and courts and of executive police. It might as well be particularly difficult for the civil law practitioner to understand these particular conditions, but if he had included international law in his studies, as he should have done, he would know the specific nature of international system (Proceedings of the American Society of International Law at its Annual Meeting, 1960). The argument for diplomacy by Thayer goes on as the lawyer “Faces a problem he attempts to solve” Further quoted “by legal agreements in which every contingency is foreseen and every detail is strictly defined. He seeks to regulate affairs by hard and fast formulas within a completely ordered system.” (Proceedings of the American Society of International Law at its Annual Meeting, 1960) His Excellency said that he however was not impressed per se with the statement, the formulation and interpretation of agreements is a basic element of the diplomatic technique. The capability of finding adequate language for an agreement is greatly improved by the study of law. That holds true whether the special circumstances require a precise formula or an intentionally vague idea as is at times required by diplomatic tactics. Fastidiousness and inept vindication are by no means inherent ingredients of the legal mind. The legally inexperienced amateur is very often inclined towards the strive for legal perfection that the normal intelligent lawyer is (Proceedings of the American Society of International Law at its Annual Meeting, 1960). An experience of his was referred to by him, in 1954 at the Paris Conference,

2020 Handbook on Artificial Intelligence and International Law

which led to the inclusion of Germany in NATO & the Western European Union & the termination of the occupation regime in Federal Republic, right in the middle of this conference negotiations were blocked by a dispute on the question of which legal basis the Western forces in the Federal Republic of Germany were to be stationed. Would it be a reserved occupation right of the three Western Powers or would it be on a contractual basis with the sovereign Federal Republic of Germany? A heated debate ensued between the foreign misters on the principle of sovereignty and on the legal implications of this principle. The formula used for settlement of the Dispute waould satisfy everyone except a legal dogmatist, nevertheless, the formula was suggested by John Foster Dulles – a very shrewd old lawyer. (Proceedings of the American Society of International Law at its Annual Meeting, 1960) There has been & there will be one legitimate complaint – one serious problem that needs consideration. Oftentimes a legalistic approach in the field of foreign policy and international relations is an approach which has been criticized by eminent experts & writers on political history & diplomacy. George F. Kennan did so in his book American Diplomacy, & Hans J. Morgenthau in several of his publications did so. In his Excellency’s view there was always some considerable extent of justification in this criticism. There are some concepts of “peace through law”, of a legalized world organization & of universal jurisdiction of a World Court which actually are unrealistic. It is very often occurrence that the identification or association with such concepts causes the suspicion of the professional career diplomats regarding the lawyers. They need to realize, however, that it is very unfair to identify every lawyer with such a concept of international order. Beyond this seldom legitimate complaint about the legalistic approach of the lawyer, there resides one other problem (Proceedings of the American Society of International Law at its Annual Meeting, 1960). It is a moral problem of the lawyer-diplomat. Professor Quincy Wright, a had rightly pointed this out, In his study on “The Role of International Law in Contemporary Diplomacy.” He had said that, “Diplomacy implies devotion by its practitioners to the national interests in their respective states. It is therefore contrasted with international law which implies that respect for the international legal order is an end superior to the national interests of the State.” Indeed, a certain conflict of loyalties can occur, but this possibility fails to exclude & fails to disqualify the lawyer for diplomatic business. Since, this is a moral problem which is not at all unique. Oftentimes, the lawyer is involved with twofold loyalty; for instance, when he cannot disregard the commonweal, the bonum commune of the state, nation & society. It would perhaps be beneficial if diplomats would get used to considering not only the national interest but

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also the interest of the international community. (Proceedings of the American Society of International Law at its Annual Meeting, 1960) His Excellency, goes on to say that from time to time it might as well be useful to include some outsiders, in a period where there is a United Nations Organization, where there is the International Court of Justice, where there are numerous political problems, intricately blended with legal problems, for instance the question of Western occupation in Berlin back then. In such dire times, every country needs lawyers in the ranks of the Foreign Service(s). (Proceedings of the American Society of International Law at its Annual Meeting, 1960) In conclusion, his Excellency, referred to yet another experience of his, relating to his switching from a legal profession to the Foreign Service. In 1948 he published a small book proposing & outlining an occupation statute for Germany. When later, in 1949, the Occupation Powers established an Occupation Statute; it did not entirely correspond to his ideas. But it happened to realize the basic idea of a legal order binding upon the German populace. To participate in the negotiations liquidating that Occupation Statute, the Federal Government called upon him in 1951. The negotiations lasted until 1954, more than three years. The Occupation Statute then ended up being terminated & replaced by the Paris treaties. He had to stay for these activities & the further ratifications in Parliament in 1955 & later for their execution. His deep conviction later on was that the critical situation of Berlin caused the legal titles to become decisive & politically significant, as a symbol of Western determination to resist the threats & the unilateral actions violating the very precarious international order of the postwar period (Proceedings of the American Society of International Law at its Annual Meeting, 1960). Following the speech, the Indian Ambassador to the United States – Mahomedali Currim Chagla was invited to enlighten the audience. He had been listening to the previous speech & replied by saying, “As I was listening to the eminent Ambassador of the Federal Republic of Germany, I had the sick feeling that I was the same kind of animal myself.” He went on – “I said to myself, as a lawyer I must be completely unfit to be a diplomat. But, my drooping spirits revived when I listened to the second part of the speech and I feel that you have called me, not because of the fact that I am an ambassador but because of the fact that I have been a lawyer, even if I am not a lawyer now.” Thus, the Indian Ambassador also, expressed with subtlety the contentions that he had towards being a diplomat & a lawyer. (Proceedings of the American Society of International Law at its Annual Meeting, 1960)

2020 Handbook on Artificial Intelligence and International Law

Diplomacy as a Social and State-Centric Practice

In the Sociological context, the essence of diplomacy can be said to be “subtle behavioral interaction”. Some people in their interactions with each other tend to utilize techniques that aim at the peaceful resolving of their disputes. The roots of the present practices in this field, diplomacy can be observed in the mutual relations of even the primitive people, including the function & significance of hostile contacts, attitudes towards strangers & guests, primitive “diplomats” (messenger/heralds/envoys), peace negotiators, & war emissaries. The continuance & support of pacific relations by magic & religious ceremonies, & trade as a form of promotion of inter-tribal relationships & for resolving o disputes among autonomous groups. From these primitive forms of amicable ways & practices arose the developed folkways & mores of intertribal contacts eventually arose international law. The states of the ancient world made intermittent usage of heralds & envoys & even at times rendered them the same degree of inviolability & respect as relished by present-day diplomats. In today’s modern form, the art of diplomacy came up when the city states of northern Italy began sending professional diplomats for permanent residence to the established States during the Renaissance period. Consequently, the regularized systems of diplomatic representations were evolved. A classified & universally recognized system of representation was cradled by the Congress of Vienna (1815) & Aix-jaChappele (1818). At these conferences, diplomatic agents were classified into four ranks. Namely, • • • •

Ambassador Extraordinaire & Plenipotentiary & Papal Legates or nuncious; Envoys extraordinaire & ministers plenipotentiary; Minister Residents; Charges d’affaires.

The first three categories were accredited to the head of the States to which they were sent; but the latter category was accredited to the minister of foreign affairs. Sociologically, it is imperative to observe that these distinctions of rank are still followed today but their chief importance is for ceremonial purposes. There happens to be no essential difference in function. Each State may send any grade of diplomatic agents, per their discretion – to another state, although in practice, reciprocity is commonplace. The professional diplomat is generally any individual who incurs at least some small income of his own. For the higher posts however, diplomats are often chosen from outside the service, as they might have to spend more than their allowance warrants on entertainment. In the U.S as well as U.K, the appointments are made usually as a reward for political services. In addition,

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most ranking diplomats happen to be highly educated, or members of nobility/royalty. From the era of Francis I of France to the recent times, diplomacy has been exhibited to be a practice generally by persuasive, dignified & courteous men. Often have they been high dignitaries of the Churches, consciously characterized by their universal outlook, aristocrats of rank, & often men of outstanding abilities. Patriots & military men have been chosen as diplomats often & continue to be chosen so as they are men of the world, often known in the field of arts & letters, for their refinements, & acquaintance with the customs of Courts & Countries. Naturally, the diplomatic circles have become known for their tact, customs, subtlety, courtesy, wit, & charm. All in all, the traditional Western diplomats have been men of wealth or individuals with titles or bearing names carrying ancient dignity, as exhibited, for instance from the Diplomatic List (Issued by the Department of State in 1939): Sir Ronald Lindsay (Great Britain); Senor don Manuel de Frerey Santander (Peru); His Highness Prince Eugene de Ligne (Belgium); Senor Don Juan Francisco de Gardenas (Spain); Marquis Alberton Rossi Linghi (Italy); Mr. Andre de Laboylaye (France); Lieutenant Colonel Count Marcel Stomm (Hungary); Baron van Bregel Doublas (Netherlands), etc. The consciousness of the diplomatic tribe is exhibited through the activities of the “Leisure Class”, as described vividly in the works of Veblen, while relishing the heightened features added by prestige granted to them as the representatives of ruling sovereigns, with all the prejudices of the “favored” & “higher” ones in comparison to the “lower” (undiplomatic) classes. Along with due emphasis on the rounds of time-consuming activities which become ends in themselves, while originally intended as means to ends, coupled with social values which are quite isolated from the rest of the cultural groups of each capital in the world. In fact, the hierarchies of the values place the diplomats at the peak of the political & social structures, as they represent the most universally accepted social value – the State & its power. The social awareness of the apparent “high standing” of the diplomatic tribe is increased by the special privileges accorded to the diplomats, who live with lifestyle standards unavailable to the general public. For example, they are graced with immunity from all laws of the country where they are accredited. Traffic regulations are not binding on the vehicles carrying distinguished license tags. The embassies & Chanceries are considered provinces of the respective countries & the properties are thus tax free. Owing to the fact that no taxes need to be paid, the finest imported goods can be bought by them at exceptionally low prices. As personal representative of the heads of the governments – an Ambassador is entitled to special dignities, & deal directly with the Secretary of State or Foreign Minister. Being men who are continually engaged in cooperative activities, they develop folkways, mores, habits, disciplines & morals, endowing each of them with

2020 Handbook on Artificial Intelligence and International Law

unity with the respective people, & a sort of “Personality or character”. This “diplomatic personality” is the result of both, accident & environment, depending upon the type of the individual who historically assumed & assumes control; while the environment coaxes them into conformity with what is expected of them vis-à-vis both practical results & the representation of sentimental ideals. Diplomacy is an old profession; the members unconsciously or subconsciously submerge their own personalities & adopt the persona of the organization while acting as a part of it. Once formed, these attitudes establish the trait of persistence in all living beings, causing them to grow & expand. The ceremonies which an organization adopts in order to reconcile its ideas are addressed to its own members & not outsiders. Thus, seldom are they convincing to the critics. The diplomat, above all – represents the State. This as a social institution has quite the glamor of its own, as an independent center of Social Power. The diplomat represents the traditionally sanctioned authorities, which leaves no room for the idea that “things could be otherwise”. The wielder tends to act as a timeless embodiment of the Superiority. The diplomatic community is based upon “espirit”, whose norms & institutions demand absolute authority, critical opinions upon which are tabooed. Forms of social intercourse, pastimes, patterns of speech & dress are reserved for it while the whole structure is held together by the glue of highly formalized rituals, maintenance of a vertical distance which dominates the groups’ thinking, characterized by strict standards of conduct, prescribing formal etiquettes for each & every occasion, frowning upon spontaneous, impulsive behavior which is rated as “vulgar”. Ceremony is an integral aspect of such group behavior. Furthermore, ceremony in diplomacy is social intercourse which achieves the object of keeping ordinary men at a respectful distance from the source of authority. While diplomacy being a solemn affair requires endless rounds of ceremonies, which promote motor reactions expressing attitudes off deference to authority or submission to tradition, manipulated in the interests of prestige. They proceed as per the design & proper decorum & the aesthetic features of rituals require not due observation. The repetitive fine behaviors of greeting & parting provide a smooth interactional relationship which provides a sense of solidarity, of the familiar; they provide a solid & predictable basis upon which conversation & overt action can proceed. In the likeness to other habits, they save time & energy, relax tensions or drives which might otherwise disrupt or redirect a dynamic interactional relationship. Differentiation in the hierarchically organized stage further influences attitudes towards cultural objects & all social norms & institutions, in turn producing definite essential features of the group culture – forms of social intercourse, pastimes, and patterns of speech. Even the very language used to casually converse in tends to become formalized, stereotyped & stylized.

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Traditional, Political & Adjudicatory Means of Dispute Resolution

The doctrines of peaceful settlement of disputes are fundamental to the UN system. Enshrined in numerous conventions & well-established as per customary law principles, a ‘dispute’ was defined by the Permanent Council of International Justice in the Mavromattis case of 1924 to be “a disagreement on a point of law or fact, a conflict of legal views or interests between two persons”. (International Dispute Settlement: A Network of Cooperational Duties, 2003) The principle of peaceful settlement relates to international disputes, & not national or domestic ones. The internationality of the dispute does not lie within the established bounds of territorial matters or jurisdictions, but lies in the legal substance of the dispute. They are those in which the rivaling claims are grounded upon international laws, additionally, an international dispute arises in case the rivaling bodies are constituted & function under the purview of International & not purely national laws or rules or regulations. (International Dispute Settlement: A Network of Cooperational Duties, 2003) Political Means of Dispute Resolution

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Negotiation – Negotiation essentially refers to communication, interaction & effective bargaining between the contesting parties free from the influence of third parties. Clauses in favor of this method of dispute settlement are well included in most conventions & treaties. Negotiations aim to produce a “consensual” result, which draws its legitimacy from consent. However, it can often be tainted by power disparities (International Dispute Settlement: A Network of Cooperational Duties, 2003).

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Fact-finding – The purpose of this method is to enable the resolution of disputes that arise mainly from differences in opinions of the parties about facts. The methods aim at properly elucidating the facts. This method has been positively reappraised in the recent times. International conventions like the United Nations Convention on the Law of the Non-Navigational Uses of International Watercourses of 1997 further provide for compulsory fact-finding in case any dispute arises. Inquiry Commissions are established, both on the national plane following the collapse of illegitimate regimes (such as Truth Commissions in South Africa) & on the International plane as well. This has been characterized as a “hybridinstitution” located in a no man’s between domestic & international law (International Dispute Settlement: A Network of Cooperational Duties, 2003).

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Mediation, Good Offices & Conciliation – The accepted description of International mediation is provided in Article 4 of the Convention for the Pacific Settlement of Disputes of 1907. It reads as:

“The part of the mediator consists in reconciling the opposing claims and appeasing the feelings of resentment which may have arisen between the States at variance.” (International Dispute Settlement: A Network of Cooperational Duties, 2003). Good offices are alike mediation & are not specifically elucidated in Article 33 of the UN Charter. Conciliation varies from mediation by very small differences, such as the differences in strategies owing to their respective state of institutionalization, or the increasingly limited mandate of the Conciliator relatively to a mediator, whereby a conciliator cannot take sides or recommend solutions to the parties. However, in case of mediation too any advices given to the parties by the mediator are of a non-binding nature & therefore it’s essentially the partners who reach a solution on their own. These alternate methods of dispute settlements are preferred over the traditional adjudicatory methods of arbitration & litigation as they are much less adversarial & create win-win scenarios & solutions. With regards to international law, & dispute settlement at an international level, the non-binding nature of the advices in ADR also works towards the object of protecting sovereignty of the States. (International Dispute Settlement: A Network of Cooperational Duties, 2003). Adjudicatory Means of Dispute Resolution • Litigation – The traditional methods of litigation might prove to be efficient in international courts, however, they disrespect & infringe the sovereignty of States. • Arbitration – International arbitration can be categorized as follows – ─ State-State Arbitration – Often practiced by the WTO panels & the Appellate Body, an instance would be the Ethiopian-Eritrean Boundary Commission & a Claims Commission created in 2000. The establishment of these commissions was evidently significant, as they were both developing nations, & it indicated decrement in the sovereignty conscious reserved attitude of the third world countries. However, the funding for such bodies is an issue. ─ Mixed Arbitration/State-Private Party Arbitration – This method of arbitration is taken recourse to in matters concerning State(s) & private entities as parties, mostly in case of commercial issues. The OECD Guidelines 2000, as well as the Stability Pact for South Eastern Europe,

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2000 encourage Mixed Arbitration. Two instances of mixed arbitration are the US-Iran Claims Tribunal, established after the hostage crisis of 1979 (operating with respect to a modified version of the UNCITRAL Arbitration Rules) & with regard to investment dispute the ICSID framework. State-State & Mixed Arbitration, both are pigeonholed as a continuing internationalization of arbitral procedures. Traditionally, the arbitral process was included in special agreements between the parties, governed by the lex loci arbitri, the law of the official seat of the tribunal, hence, the domestic procedural law of particular states.

Legal Background Concept of Diplomacy & Diplomatic Agreements

Fundamentally, diplomacy refers to the established methods of impelling decisions & behaviors of international governments & the global citizenry through the utilization of dialogue, negotiation, & other measures without resorting to wars or acts of violence. Modern practices of diplomacy are a product of post-renaissance European state system. Diplomacy used to mean the conduct of official (usually bilateral) relationships between sovereign entities & states. Consequent to the success of negotiations, the results are exemplified in international instruments. There are many such instruments such as, Treaties – treaties are the most solemn of them all, they are written agreements between states as parties or states & private entities as parties, under the purview of international laws. The treaties are akin to the predominant contracts in civil law. Treaties may be bilateral or multilateral & are registered at the UN. International organizations also conclude treaties with individual states & with each other. Conventions – a convention is basically a multilateral instrument having a lawmaking, codifying or regulatory nature. Conventions are generally negotiated under the umbrellas of international entities or a conference of states. The UN & the many agencies thereof, negotiate conventions, like the Council of Europe. Treaties & Conventions further require ratifications which is an executive act of final approval. In the U.S., the Senate has to consent by a two-thirds majority. In Britain, treaties are housed at the House of Commons for 21 days before ratification; other countries have similar procedures as well. In case of Bilateral Treaties, ratifications are exchanged; otherwise, they are deposited in a place named in the text, & the treaty comes into effect when the specified numbers of ratifications are duly received.

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Protocols – a protocol essentially prolongs/amends/supplements/supersedes an existing instrument. It may contain details with regards to the application of an agreement, or optional arrangement for extension of some obligatory convention, or some technical instrument acting as an annexure to a general agreement. It might as well substitute as an agreement or an exchange of notes, which can be utilized for recording of a bilateral agreement or the modification thereof. International instruments have thrived ever since the Second World War. Starting from 1945 to 1965, there were 2,500 multilateral treaties, more numerous than ever in the previous 350 years. Pursuant to the gaining of independence of one country after another, the trend continued. Most multilateral agreements are negotiated through conferences. The negotiations are abundant & often long-drawn-out, sometimes giving rise to multivolume treaties even. Sources of Diplomatic Law

The laws, methods, procedures of diplomacy are enshrined in numerous legislations adhered to by different provinces all over the world, some of which are: • Geneva Conventions of 1949 & Additional Protocols (International Committee of the Red Cross) – ─ Geneva Convention (I) on Wounded & Sick in Armed Forces in the Field, 1949. ─ Geneva Convention (II) on Wounded, Sick & Shipwrecked of Armed Forces at Sea, 1949. ─ Geneva Convention (III) on Prisoners of War, 1949. ─ Geneva Convention (IV) on Civilians, 1949. ─ Additional Protocol (I) to the Geneva Conventions, 1977. ─ Additional Protocol (II) to the Geneva Conventions, 1977. ─ Additional Protocols (III) to the Geneva Conventions, 1977. • ABC of Diplomacy, by the Swiss Federal Department of Foreign Affairs (Schweizerische Eidgenossenschat Confedaration suisse Confedarazione Swizzera Confederaziun svizra, 2008). • ‘Belhaj V. Jack Straw’ – Judgment of the Court of Appeal 30/10/14 (Royal Courts of Justice Strand, London, WC2A 2LL, 2014). • Berne Convention for the protection of literary & artistic works (World Intellectual Property Organization (WIPO), 1979). • Consular Relations Act, 1968 (legislation.gov.uk).

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• Convention for the Protection of Human Rights and Fundamental Freedoms (Council of Europe Portal, 1953). • Convention on Diplomatic Asylum (OAS) (Organization of American States). • Conventions on the rights of the child (United Nations Human Rights Office of the High Commissioner, 1990) • Customary International Law. • Declaration on Principles of International Law concerning Friendly Relations and Co-Operation among States in accordance with the Charter of the United Nations (United Nations General Assembly, 1970). • Defence Committee of the House of Commons Report – ‘The situation in Iraq and Syria and the response to al-Dawla al-Islamiya fi al-Iraq al-Sham (DAESH)’ (House of Commons, 2015). • Diplomatic Privileges Act 1964 (legislation.gov.uk, 1964). • Escaping Diplomatic Impunity – The case for Diplomatic Law Reform by Eirwen-Jane Pierrot. • European Convention of Human Rights (European Court of Human Rights, 2010). • Human Rights Act 1998 (legislation.gov.uk, 1998). • Intelligence Services Act 1994 (legislation.gov.uk, 1994). • The doctrines of Jus Cogens & Obligatio Erga Omnes (INTERNATIONAL CRIMES: JUS COGENS AND OBLIGATIO ERGA OMNES, 1996). • International Treaties & Agreement (US Department of State website). • Minsk Protocol, Minsk II. • New Zealand Ministry of Foreign Affairs & Trade – Diplomatic Privileges and Immunities. • Norwich University – Past Peace Treaties & Negotiations. • Regulations of Investigatory Powers Act, 2008. • Rome Statute of the International Criminal Court. • State Immunity Act, 1978 (legislation.gov.uk, 1978). • The Diplomatic & Consular Premises Act, 1987 (legislation.gov.uk, 1987). • The Universal Declaration of Human Rights (United Nations, 1948). • Travaux Preparatoires. • United Nations and the Rule of Law. • United Nations Diplomatic Conferences. • United Nations Security Council Documents.

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• UN Office of Legal Affairs Handbook on the Peaceful Settlement of Disputes between States (Office of Legal Affairs - Codification Division, 1992). • US Department of State Consular Notification and Access Manual. • Vienna Convention on Consular Relations, 1963 (United Nations, 1963). • Vienna Convention on Diplomatic Relations, 1961 among others. Among the foremost & most widely known of the legislations are the Vienna Conventions on Consular Relations of 1963 & the Vienna Convention on Diplomatic Relations of 1961. The Vienna Convention on Diplomatic Relations (VCDR) was signed on the 18th of April, 1961, & came into effect dated 24th of April, 1964, while the Vienna Convention on Consular Relations (VCCR) was signed on 24th of April, 1963, & came into effect dated 19th of March, 1967. The two conventions form the very core of international diplomatic & consular laws. The VCDR codified customary rules on bilateral diplomatic relations between provinces. Whereas, its provisions have largely been integrated as parts of general international laws themselves. The conventions have 190 signatory state parties, & thus their application is truly global. They provide for a complete framework aimed at the establishment, maintenance, & termination of diplomatic relations on the precipice of consent between independent sovereign States & they have firmly established themselves as coveted cornerstones of modern international relations. Two optional protocols were further added to the conventions namely, the Optional Protocol concerning the Acquisition of Nationality & the Optional Protocol concerning the Compulsory Settlement of Disputes. The UNs’ striving towards the codification of international law awarded the VCCR with utmost significance. A lot of the provisions in the conventions have earned recognition as customary laws, but not all of them, as of 2014 – 177 States have ratified the Convention. The VCCR provides for a universal framework of minimum standards for the purposes of conduct of consular relations. The VCCR also recognizes the validity of agreements, be it bilateral or regional that have been in force even before the VCCR came into effect, but however, it negates any agreements that supplement/extend/amplify provisions of the VCCR. Functions, Rights & Privileges of Diplomatic Agents

The practice of deploying diplomatic agents has been conducted by States since ancient of times. In the Indian context, “Doots” / “Rashtra Doot’s” are sent from one province (Rashtra) to another. In the ancient times, however, the diplomats were sent to other provinces for permanent residence. The practice of sending

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diplomats for permanent residence started in the Century. By the latter half of the 17th century, permanent legation became a general institution. Certain rights & duties alike in nature to each other were provided to diplomatic agents (Asthana, 2019). The Congress of Vienna of 1815 took the initiative & codified the customary rules of International Law in the ranks of diplomatic representatives. The practices of diplomacy continued in development after 1815 following the establishment of the UN. The task for codifying the law was given to the International Law Commission. The commission prepared & submitted the draft article to the General Assembly. The General Assembly in a conference in 1961 adopted the Vienna Convention on Diplomatic Relations. (Asthana, 2019) Functions of Diplomatic Agents. The various functions of diplomatic agents are mentioned under Article 3(1) of the Vienna Convention of Diplomatic Relations, 1961 as• Representation – Diplomatic agents represent the policies & beliefs of States who deploy them to the State to which they are accredited. The function of representation is chiefly delegated to the head of the mission. Oppenheim, in his book, said that • “diplomats are the mouthpiece of the head of his own State and the Foreign Minister for communication to be made to State where they are dispatched.” (Asthana, 2019) • Protection – Diplomatic agents are entrusted with duties to protect the rights & interests of sending State & also of the nationals of the State. The limits to the powers of the diplomatic agents are not prescribed by International Law but by the Municipal Law of the State in question (Asthana, 2019). • Negotiation – Diplomatic agents are entrusted with the most essential tasks of negotiation. In general, the head of the diplomatic mission is supposed to negotiate on various aspects on of the sending State with the State to which they are deployed, to facilitate friendly relations. Diplomatic agents are required to communicate the outcome of the negotiation to their mother State from time to time (Asthana, 2019). • Observation – Diplomatic agents are required to observe the events & occurrences which take place or which may take place in the province to which they are deployed. Following the observations of the events, they are required to make periodical reports to the government of sending States (Asthana, 2019).

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• Promotion of Friendly Relations – Diplomats are to promote friendly relations between the deploying State & the State to which they are deployed. They have the supplementary function to develop the social, cultural & economic relations between the two States (Asthana, 2019). • Consular Functions – Vienna Convention states that diplomatic agents are to perform consular functions which are to be allotted to them from time to time such as death, birth & marriage registrations of the subjects of home State, issue of Passports, etc. (Asthana, 2019) The Basis of Diplomatic Immunities & Privileges. International law awards diplomatic immunities to diplomatic agents against the exercise of jurisdiction by the receiving States. The principles governing diplomatic immunities & privileges are among the most ancient & universally recognized principles of International law. Varying schools of thought have varying perspectives vis-à-vis the basis for giving immunities to diplomatic agents. The different views of the different jurists led to the rise of three important theories which are as follows: • Extra-territorial Theory – The theory is also referred to as the fictional theory. As per this theory, diplomatic agents are considered not to be within the territorial jurisdiction of the State to which they are deployed, but are subject to the jurisdiction of the deploying State. Extra-territorial theory represents the belief that although diplomats are physically present on foreign soil, they remain for all purposes on the soil to which they belong (Asthana, 2019). • Representational Theory – As per this theory, diplomatic agents are considered as personal representatives of the sovereign heads of the sending States. Consequently, they are awarded the same degree of privileges & rights which are awarded to the heads of the deploying States (Asthana, 2019). • Functional Theory - As per this theory, diplomatic agents are awarded immunities owing to the nature of their functions. The duties they are entrusted with are far from easy. In other words, the actions & duties of diplomatic agents typical & of special natures. They are allowed immunities from limitations, legal & otherwise, in order to ensure effective performance of the tasks that are allotted (Asthana, 2019). The Rights and Privileges of Diplomats. The rights & privileges granted by the Vienna Convention on Diplomatic Relations of 1961 laid down are:

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• Inviolability of Diplomatic Agents – the inviolability of diplomatic agents is a principle recognized under International Law since much before the adoption of the Convention of 1961. Article 29 of the VCDR laid down that – “the person of a diplomatic agent shall be inviolable” (Asthana, 2019). In simpler words, a diplomatic agent shall not be liable to any form of arrest or detention & the obtaining State is to treat him with all due regard & is to take all suitable form to prevent an attack on his personal freedom & dignity (Asthana, 2019). The government of the receiving State by virtue of Article 29 of the VCDR is bound under a duty to desist from any form of conduct which is injurious to the diplomatic agents & also under a duty to avert such injurious conducts attempted by others (Asthana, 2019). The immunity is however not absolute. The receiving State has the power to arrest or detain the diplomatic agent in exceptional cases. E.g. a drunken diplomat with a loaded gun in a public place is liable to be arrested, or a diplomatic agent committing an act of violence disturbing the order & peace of the State in a manner that it becomes necessary to restrain him for the purpose of preventing similar acts can be restrained (Asthana, 2019). • Inviolability of Staff of Mission – Supplementing the immunities awarded to the Diplomats & the head of mission, Immunities are also awarded to the staff of the mission, as defined in article 1 of the VCDR & Para 2 of article 37 of the VCDR. These articles ascertain the enjoying of immunities & privileges mentioned from Article 29 to Article 35, in case the staff are not the nationals of the State. Therefore, the administrative & technical staffs enjoy personal inviolability as per Article 29, inviolability of residence as per Article 30(1), immunity from criminal jurisdiction as per Article 31(1), exemption from certain taxes & duties as per Article 34 & immunity from civil & administrative jurisdiction while discharging duties as per Article 31(1). Para 3 of Article 37 of the VCDR awards immunities to the service staff in case they are not the nationals or permanent residents of the receiving States, with respect to taxes & duties on emoluments received & exemptions on social security provisions (Asthana, 2019). • Inviolability of Premise – Article 21 of the VCDR states that – “a permanent diplomatic mission needs premises to operate and receiving State must help the sending State to obtain the premises form mission.” (Asthana,

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2019) The deploying State has the right to utilize its flags & emblems on the premises as per Article 20 & Article 22 of the VCDR, which suggests that the customary rule of International Law suggesting that the missions is not violable in the words – “the premises of the mission shall be inviolable” (Asthana, 2019). Furthermore, Article 30 provides that – “private residence of a diplomatic agent shall enjoy inviolability” (Asthana, 2019). No officers of law enforcement shall be allowed to enter the place of residence of diplomats without the consent of the head of the mission. However, the inviolability is not absolute – it can be excused in certain scenarios. Article 41 of the VCDR itself states that – “premises of the mission should not be used in any manner as incompatible with functions of mission or by rules of general International Law.” (Asthana, 2019) Thus, if the inviolability of the premises is abused, then the receiving State shall not bear it passively & shall have the power to take all necessary steps to inhibit the any such abuse. • Inviolability from being a Witness – Diplomats enjoy complete immunity from summons as witness to Courts of Law be it in case of civil matters or criminal matters. They are also immune to being asked or ordered to give evidence before the commissioner. Nevertheless, they might have to appear before the Court if they wish to waive their immunity. Article 31(2) states that – “diplomatic agent is not obliged to give evidence as a witness” (Asthana, 2019). • Immunity from Taxes & Customs Duties – Article 34 of the VCDR states that “diplomatic agents shall be exempted from all dues and taxes, personal or real, national, municipal or regional” (Asthana, 2019). Primarily preceding the convention, these rights were awarded to the agents as Courtesy but the Convention later incorporated it with more precise definitions (Asthana, 2019). • Immunity from Inspection of Personal Baggage – The bags & cases used for sending articles/letters/documents to the deploying provinces or any other missions abroad by the Diplomats are treated as diplomatic bags. Para 3 of Article 37 of the VCDR states that – “diplomatic bag should not be opened or detained.” (Asthana, 2019) Nevertheless, as per Article 36 Para 2, the right so conferred is not absolute. It suggests that –

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“general practice of exempting the diplomats’ personal baggage from a custom inspection is qualified by the provision that inspection can be conducted in presence of a diplomatic agent or his agent if there are serious grounds for suspecting that the article is not for official use.” (Asthana, 2019) • Freedom of Communication – Diplomats are at freedom to communicate any information for official purposes to the States to which they are deployed. Article 27 of the VCDR states that – “the freedom of communication also involves the use of code messages and couriers.” (Asthana, 2019) •

Freedom of Movement & Travel – Article 26 of the VCDR authorizes the movements of diplomats in the territory to which they are deployed subject to the International Laws & Rules in propagation in the province in question, concerning security zones (Asthana, 2019).

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Right to Worship – Article 3(1) of the VCDR empowers the diplomats with rights to worship any religion they profess within the mission premises or residence. However, they are inhibited from inviting any nationals of the State to which they are deployed to participate in the Worship & have no right to preach their religion (Asthana, 2019).

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Immunity from the Local Jurisdiction – Diplomatic agents enjoy immunities from all jurisdictions of the Local Courts, extending to both Criminal as well as Civil jurisdictions (Asthana, 2019).

Article 31, Para 1 of the VCDR provides for enjoying of immunities by diplomatic agents in the State to which they are deployed. Therefore, the receiving State possesses no such rights to prosecute &/or punish diplomatic agents. This is a well-recognized principle of International Law (Asthana, 2019). Diplomacy & Multilateralism. April 24th is celebrated as International Day of Multilateralism & Diplomacy for Peace; the occasion was established on the 12th of December, 2018 through resolution A/RES/73/127 & was first observed on the 24th of April, 2019 (United Nations). Embodied in the UN Charter & the 2030 Agenda for Sustainable Development, the preservation of values of multilateralism & international cooperation is paramount. The international norms & rules-based systems that have directed nations through since the last seven decades must escalate to defeat the intensifying challenges of protectionism & isolationism. Global issues in the likes of climate change, geopolitical tensions, humanitarian & migratory crises happen to be cross-cutting, in turn implicating the values &

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interests of nations & necessitating collective attention & action. Technological advancements as well have impacts on political & socio-economic landscapes & interstate relations (United Nations). The International occasion is a reaffirmation of the UN Charter & the principles thereof, for resolving disputes among countries via peaceful means. It goes on to acknowledge the usage of multilateral decision-making & diplomacy for the achievement of peaceful resolutions to conflicts among nations (United Nations). The United Nations Organization came into force in 1945, consequent to the devastation of the Second World War, bearing in mind one objective – to maintain international peace & security. The UN Charter states one of the UNs’ purposes & principles is the commitment towards the settlement of disputes through peaceful means & the determination towards saving the succeeding generations from the blight of war (United Nations). The prevention of conflicts still remains a relatively under-publicized aspect of the UNs’ work. In the meantime, the most efficient & the most desirable employment of diplomacy is the easing of tensions before such tensions result in a conflict, or, in case a conflict breaks out, to act swiftly to contain it & to resolve its underlying causes. Preventive diplomacy is increasingly significant for supporting the United Nations’ efforts towards assisting the peaceful settlement of international disputes which would otherwise cause conflicts to break out (United Nations ). The Commitments to multilateralism & International peace & security has also been reaffirmed by most significant world leaders in the General Debate in September 2018, this commitment was further reinforced in the discussion during the High-Level Dialogue on Renewing the Commitment to Multilateralism on 31 October 2018 (United Nations ). The UN Secretary General Antonio Guterres has famously remarked – “It is not enough to proclaim the virtues of multilateralism; we must continue to show its added value. International cooperation must adapt to changing times.” (United Nations ) Quantitatively, multilateralism basically refers to a process of organizing relations among groups of three or more entities/States. Beyond this, multilateralism is typically considered to encompass certain qualitative elements or principles that silhouette the character of the arrangement or institution. These principles are in the likes of indivisibility of interests among participants, commitment to diffuse reciprocity, & a system of dispute settlement intended to enforce a particular mode of behavior. The pioneers of multilateralism are considered to be the WTO (World Trade Organization) & the NATO (NorthAtlantic Treaty Organization) along with the UN. In order to understand the nature of multilateralism better, it can be compared

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to bilateralism. A good illustration for such bilateralism are the commercial policies of Nazi Germany – in which, the Germans negotiated agreements bilaterally with other countries, postulating which goods & services were to be traded, the prices thereof & the quantities to be swapped. Owing to this, a significant number of nations were connected by trade agreements, while Central Germany acted as a hub. In comparison to this, the multilateral commercial regime based upon the General Agreements on Tariffs and Trade (GATT) of 1948, utilized the most-favored nation (MFN), in which case, third parties were treated in a much more inclusive manner than the bilateralism resorted to by the Germans. The third parties were granted equal treatment by virtue of the MFN Clause, therefore, it can be said that the German system was established entirely around systematic discrimination, whereas, the GATT assured non-discrimination for all contracting parties. With respect to security set up, the rules of multilateralism are best embodied in a collective security system such as NATO, in which a war against one state is reasoned to be a war against all states, ensuring that any act of aggressiveness against a member of the collective system is met with a consequence from all the members. There is a concept of indivisibility of interests at play. In security arrangements – peace is treated as indivisible, i.e., no participating member can be at peace while another is at war. In terms of commercial policies, the norm of the MFN make the trade system an indivisible whole. Supplementary to the principle of indivisibility of interests, multilateralism is measured to give rise to expectations of diffuse reciprocity which fundamentally means that certain situations have an inherent expectation that there shall not be an equivalence in obligations or concessions in an exchange, but rather, a balance to be expected over an ongoing, potentially indefinite, series of exchanges with a group of partners. For e.g., in the security system, members do not suppose that they shall be compensated for the military resources they might disburse for the purposes of defending an exposed member country, their compensation lies in the prospect of being defended by other members if & when they themselves are attacked. In order to ensure that the member States feel assured of the returns of treating their interests as indivisible, multilateral agreements, the international organizations tend to incorporate some mechanisms to facilitate the action of countries in accordance to the expected & established norms. The principle of dispute settlement forms the third principle associated with multilateralism. A plethora of methods for ascertaining compliance are utilized, such as peer review – suiting informal agreements, or creation of formalized bodies that shall preside over the alleviation of grievances. Virtual Diplomacy. If we consider diplomacy to be a way to set & achieve foreign policy goals, then

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it goes without saying that the fundamental & foremost task of diplomats is to provide information & to negotiate. Intelligence gathering lies right at the top of priorities. Therefore, any & all updates or upgrades in technology or communication need to be adapted to by the diplomats as soon as they roll out. The invention of the Telegraph, shortened the vast amount of time that it took diplomats to communicate with each other, & made it infinitely cheaper. The coming in of the telephone revolutionized the landscape as well; information could further be transferred & received within seconds. Ships with sails were replaced with steamships. Lastly, all of it has been replaced by the Internet. All such developments have been giant strides towards advancements in the information & communication technologies infrastructures. The audience now is undefined & so is the amount of information that can be shared or received. Nevertheless, these brilliant changes have brought forth needs for revisal in foreign policies (Grech, 2006). The information revolution was the World Wide Web – created by the European Organization for Nuclear Research (CERN). This enabled the sending of texts, pictures simultaneously through the internet, this had attracted business & the broad audience bases. The newer communication systems & machineries have provided a free flow of content & information. All this led to increased standards of transparency. The global citizenry has acquired by means of the internet powers to exert pressure on governmental decisions, particularly the ones concerning international committees or national interests. Thereby causing the government to be diligent & to exercise due care in ascertaining that the public trusts the government to act wisely on its behalf. The information revolution has essentially turned information & intelligence into a source of national power & influence (Grech, 2006). “Soft power is the ability to achieve desired outcomes in international affairs through persuasion as opposed to coercion. It works by convincing others to follow, or agree to, norms and institutions that produce a desired behaviour. With its emphasis on information and knowledge, the new communications environment is making soft power more practical. Indeed, the new ICTs hold the key to soft power, making it possible to appeal directly to a multitude of actors.” (Virtual Diplomacy , 2001) Nevertheless, there’s one drawback that the information revolution brings to the table – speed. The global citizenry expects an increasingly swift response to an incident or event, failure to provide which causes a response dipped in disappointment or notion of instability from the public & causes the loss of good faith & trust of the government. The globe is continually becoming more & more hectic & hastier & oftentimes diplomatic reactions & decisions need to be made not in a matter of seconds but after due deliberations & calculated assessments. Whereas, owing to the information revolution, diplomats are always subject to the pressures of the need to make a swift decision which tends

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to have side effects. Decisions made hastily, can be rushed or discrepant if all facts are not taken into consideration, or might be faulty in judgment due to not much time being spent on deliberations (Grech, 2006). Diplomatic decisions have a vast impact on the futures of the nations & the consequences are too grave to be taken lightly at the cost of speed. Political scientist Eytan Gilboa (2002) summed up the situation in the following statement: “If foreign policy experts, intelligence officers, and diplomats make a quick analysis based on incomplete information and severe time pressure, they might make bad policy recommendations. Conversely, if they take the necessary time to verify and integrate information and ideas from a variety of sources, and produce in-depth reliable reports and recommendations, they may find that their efforts have been futile if policy makers have had to make immediate decisions in response to challenges and pressure emanating from coverage and global television.” (Grech, 2006) Yet another side effect of the Information Revolution is increased risk of being misunderstood. The constant pressure on the diplomats leading them into public diplomacy, information that is traditionally aimed towards diplomats, is now to be also accessible to the general public – the non-diplomatic community. It is truly difficult for an individual who is not familiar with the diplomatic terms of practices to grasp entirely the concept & the intention of diplomats. All diplomatic opponents’ sources that continually scrutinize & criticize diplomats & diplomatic decisions are increased manifold owing to the media & public in general, coupled with non-governmental organizations & other organizations (Grech, 2006). Richard Solomon – President of the United States Institute of Peace and former US Foreign Service Officer in 2000 remarked that: “Information about breaking international crises that once took hours or days for government officials and media to disseminate is now being relayed realtime to the world not only via radio and television, but over the Internet as well. Ironically though, for policy-makers, instant dissemination of information about events both far and near is proving to be as much a bane as a bounty.” (Grech, 2006) We have witnessed the evolution & advent of the digital age which has yielded the Information Revolution which has had a significant effect on diplomacy. The effect has been so far reaching & significant that a new type of diplomacy has raised – Virtual Diplomacy (Grech, 2006). The term has been devised based upon the fact that it refers to diplomacy operated via the usage of technology & the internet, instead of traditional faceto-face technology. Virtual diplomacy is by no means to be underestimated. In 1997, Richard H. Solomon defined virtual diplomacy to be: “social, economic, and political interactions that are mediated through electronic means rather than face-to-face communication”. (Grech, 2006)

2020 Handbook on Artificial Intelligence and International Law

He further went on to clarify that “virtual” although referring to a lack of reality does not warrant the unreality of this form of diplomacy. Further, Gordon S. Smith via a report to the Virtual Diplomacy Initiative refers to this aspect: “Virtual diplomacy can be said to mean the conduct of what in the past has been regarded as classical diplomacy but that is now an activity being practiced in a different way both because of changes in technology and because it is being practiced by a broader range of people, including many who are not professional diplomats” (Smith, 1999) Virtual Diplomacy prides itself upon the improvement of traditional diplomatic functions of representation, negotiations, reporting, facilitation, & coordination. It amalgamates foreign & domestic publics & calls for the allowance of diplomacy occurring through global media & information technology. Nevertheless, opinions vary as to whether it shall ever phase out traditional diplomacy & slowly but smoothly render it redundant. However, there always is the nascent possibility. Virtual diplomatic missions are more cost effective than physical missions, they offer added convenience, they are more informative as the web does not limit the amount of information to be uploaded, they are more accurate since every database can easily be updated, they are easily locatable via the URL, & are available 24/7 (Grech, 2006). The pandemic – the far spread devastation caused by Covid-19 has not spared the UN. The spread of the coronavirus to the whole world, & the quarantines & lockdowns effected in response have inevitably prompted queries as to how the UN is functioning or shall continue to function & the very nature of diplomacy that shall come into play in these troubled times & the times to come. The UN continues to adapt to the world of virtual meetings. Diplomats & staff shall eventually be faced with questions as to whether to return to the traditional ways of conducting business once the pandemic subsides (Chowdhury, 2020). It’s to be borne in mind that changes in response to immediate crises can bring forth new norms. The influencing of international affairs & institutional responses by personalities, relationships, characters, & tradecraft, & changes that reduce the scope of personal interaction stand to have long lasting consequences for better or worse. Anyone who has spent considerable amount of time engrossed in negotiations can concur to the importance of personal interaction & can recount stories concerning a call or quiet chat that made all the difference in the world to the overall outcome (Chowdhury, 2020). Optimistically, the shift from traditional physical to virtual platforms is likely to highlight numerous opportunities for enhancement of the efficiency of UN business. Enormous resources allocated to processes & working methods that have accumulated & continue to accumulate over time are impossible to challenge once established. The pandemic might just be a much-needed catalyst required to reevaluate approaches. Demonstrating that it is possible to conduct

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great amounts of business virtually, generating & inspiring other new & creative ways to business. Opportunities to reduce travel & target remaining travel much more effectively are undoubtedly presented by the shift if made. However, it is likely that critical matters shall be lost without accompanying experts & meetings to give them context. In case the UN’s business is brought down to the standard of transmission of reports without any discussion or consideration, expertise & understanding of the matters shall suffer a bludgeoning blow. (Chowdhury, 2020) There is valid concern as to whether the UN can effectively function in a world under quarantine or post quarantine on a long-term basis where virtual spaces replace the horse-shoe table of the Security Council. The council has been meeting online informally & it’s highly likely that more business shall be conducted in this manner. Owing to the fact that the meetings are closed on virtual platforms, there are no questions as to the visibility of the Council & access by other member states, non-governmental organizations & the media. (Chowdhury, 2020) Parliamentary Diplomacy; Consequences and Limitations ‘Parliamentary Diplomacy’ has enjoyed various definitions, one of which is offered by Weiglas & de Boer as: “the full range of international activities undertaken by parliamentarians in order to increase mutual understanding between countries, to assist each other in improving the control of governments and the representation of a people and to increase the democratic legitimacy of inter-governmental institutions.” (Parliamentary Diplomacy, 2007) Various activities are under the purview of ‘parliamentary diplomacy’, they might include institutionalized or informal ways of engagement in international affairs & foreign policies by national parliaments. They include but are not limited to bilateral relations such as friendship groups, exchange of delegation, etc. Nevertheless, multilateralism lies at the heart of ‘parliamentary diplomacy’; it is more institutionalized than mere parliamentary cooperation. The institutional frameworks within which parliamentary diplomacy are practiced are the various ‘International Parliamentary Institutions’ (IPIs). Within these various IPIs, parliamentarians cooperate with each other to adopt decisions or strategies, or programs, which they implement or promote through various methods such as persuasion, advocacy or institutional pressure. Track 1 ½ Diplomacy: Contributions. There are many varying views to the terminologies & classifications within the Conflict Resolution & Peace Studies literature, nevertheless, the scene can be simplified as –

2020 Handbook on Artificial Intelligence and International Law

• Conflict Resolution – the overlying term including a number of processes, of which three broad resolutions processed can be identified – ─ Conflict Prevention. ─ Conflict Management & Conflict Settlement – aiming at containment & negotiated termination of conflict. ─ Peace-building & Conflict Transformation – aiming at reconciliation, institutional entrenchment of peace & co-existence in post-conflict settings, reconstruction & the tackling of the root causes of conflict. Two points are needed to borne in mind – firstly, conflict resolution refers primarily to violent conflicts, i.e. ethnic conflicts & civil wars, along with interstate wars, nevertheless, it also refers to non-violent conflicts or crises at the international & national levels. Secondly, despite conflict resolution including coercive methods of intervention – e.g. deployment of armed forces for the prevention of escalation, safeguarding an agreement (peacekeeping) or termination of conflicts via mediation (‘mediation with muscle’, ‘coercive diplomacy’) the stress is put upon the peaceful, non-coercive means. Keeping aside the ‘legal’ methods of conflict resolution, political peaceful methods include more formal processes like negotiations & peace talks, mediation, good offices & facilitation, conciliation, fact-finding missions, preventive diplomacy & early-warning measures to more informal practices. A widely acclaimed classification of means & actors with regards to peaceful resolution of conflicts is categorization as track -1 (‘first track’) & track – 2(‘second-track’) diplomacy. Track 1 diplomacy – “involves official governmental or intergovernmental representatives, who may use good offices, mediation, and sticks and carrots to seek or force an outcome, typically along the win-lose or ‘bargaining’ line.” (Contemporary Conflict Resolution: the prevention, management and transformation of deadly conflicts, 2005) While, Track 2 diplomacy – “involves unofficial mediators who do not have carrots or sticks. They work with the parties or their constituencies to facilitate agreements, encouraging the parties to […] find mutually satisfactory outcomes”. (Contemporary Conflict Resolution: the prevention, management and transformation of deadly conflicts, 2005) In summary, track 1 diplomacy refers basically to formal methods of conflict resolution involving official actors, such as diplomats, ministers, and heads of States & representatives of international organizations, but more casual methods involving unofficial actors, such as members of adversarial groups, members of the civil society, communities, & religious leaders, NGOs, etc., holds by track 2 diplomacy Within this literature & framework, a few more potential tracks of diplomacy have been proposed, the concept of ‘multi track’

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diplomacy has cradled, which demands that conflict resolution & peacebuilding either are or should be multilevel processes ("Quasi Track-One" Diplomacy: An Analysis of the Geneva Process in the Israeli-Palestinian Conflict, 2010). One of these multiple potential diplomacy tracks proposed is track 11/2 diplomacy. ‘Track 11/2’, true to its name – refers to the conflict resolution activities that fall between official & unofficial diplomacy. A widely recognized definition of the hybrid track is – “unofficial intervenors working with official representatives of the conflict parties” (Unofficial International Conflict Resolution: Is There a Track 11/2? Are There Best Practices?, 2009) The understanding of track 11/2 diplomacy as public or private exchanges between official representatives of conflicting’ political entities mediated by a third, non-official party are prevalent; this understanding considers this track to be a bridge between official & unofficial activities. However, this track can be conceptualized as the union of official & unofficial diplomacy as well, as not just a meeting space for two distinct sets of conflict resolution models, but an entirely new model that emerges out of the syntheses of the traditional tracks. The range of actors concerned in Track 11/2 diplomatic practices is extensive & comprises groups of officials & unofficials. Usually, the representatives of the struggle parties lies to the higher level – top decision-makers, government officials, opposition leaders, representatives of political movements, etc.

Conjunction with AI “The pace of progress in artificial intelligence (I’m not referring to narrow AI) is incredibly fast. Unless you have direct exposure to groups like Deepmind, you have no idea how fast—it is growing at a pace close to exponential. The risk of something seriously dangerous happening is in the five-year time frame. 10 years at most.” - Elon Musk (Bernard Marr) The Covid-19 pandemic goes out of hands enough to cause the whole of Israel to become infected without hopes of people being cured, & riots break out in the streets – hundreds are killed. The Israeli Ambassador stationed in the U.S.A beseeches the States to provide them with vaccines as soon as possible, & consequently U.S.A places orders in India to manufacture & provide more vaccines. However, owing to a Civil War in China, also caused owing to the pandemic, numerous Chinese nationals are forced to move out of their nation & seeks some other place to settle, finding India to be economically approximately at par with their nation, the already infected emigrate &

2020 Handbook on Artificial Intelligence and International Law

immigrate to India, thereby causing India to become another hot zone for the virus. The Indian Consul in the U.S gets word of this catastrophe, & urges the U.S to consider adjusting to the lower amounts of vaccine that India shall be able to provide, owing to the viral catastrophe that the nation itself is facing. The U.S informs this to Israel, & Israel in turn threatens to withdraw from all its arms deals with the U.S & India if they don’t receive the adequate amount of vaccines that they originally asked for. Consequent to harsh debates, & widespread rioting, war breaks out, millions die & millions more die owing to the virus. How would it have been, if an AI would’ve been able to predict the imminent migratory crisis that India is about to face? How great would it have been, if an AI could predict the high likelihood of an outbreak in Israel even when it began, & the administration could ensure vaccination of each & every individual without the immediacy coming into the picture? That’s the significance if not more, that AI holds in the field of International Relations & Diplomacy. The certainty that wars won’t break out, the serenity of disputes being sorted or being predicted even before they occur, the effectiveness of treaties among nations which don’t have even the miniscule of ambiguities, the perfectly selected candidature for the diplomatic services ensuring no misunderstandings occur & many more other things. An AI that caters to diplomats, by calculating & predicting the traffic delay based upon weather patterns & recent traffic patterns, to ensure that the diplomat reaches a dinner party – where a high ranking diplomat of a State with which this State is in dispute in, is arriving & leaving from in order to ascertain that the this diplomat is able to meet that diplomat to build rapport in order to ensure smooth & amicable disputes in the future, the same AI which targets which diplomat to consult with in order to achieve maximum possible success rate in reaching an end to the dispute, the same AI which clearly clarifies the seating arrangement to ensure that no one is unduly insulted, without the involvement of high ranking officers of the State department or foreign ministries who have much more pressing matters to entertain. That is the significance of AI in the field of International Relations & Diplomacy, if not more, & that day is not far – when an AI system might make or break a nation in terms of diplomatic endeavors &/or international relations. Therefore, ours is not to wait, ours is to prepare. We must prepare for that day, we must have policies already in place, we must understand & research as to what might come, even before it actually

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comes, in order to further improve it when it actually comes without going out of fashion or nearing extinction in competition to a machine.

Case Studies Military power, cybersecurity, threat monitoring & warfare AI

AI Applications for Defense. The U.S Department of Defense has been considering numerous applications for Artificial Intelligence. In the U.S all research & development in terms of AI is primarily delegated to organizations in the individual services, the DARPA (Defense Advanced Research Projects Agency) & the IARPA (Intelligence Advanced Research Projects Agency) (Congressional Research Service, 2020). INTELLIGENCE, SURVEILLANCE & RECONNAISANCE. AI is considered to be a particularly useful tool in intelligence owing to the large number of data sets available for analyses. For instance, the Project Marva is predicted to be able to incorporate computer vision & AI algorithms through footage from unmanned aerial vehicles in order to automatically identify hostile activity for targeting. In this matter, AI is intended towards the automation of the work of human analysts who are currently spending numerous hours sifting through drone footage for any actionable intel. Any such technological advent would undoubtedly enable the analysts to make more efficient & timely decisions based upon the data. The CIA alone has around 140 projects in under development that can presumably leverage AI in some capacity with an objective of accomplishing tasks such as image recognition & predictive analytics. IARPA has been sponsoring several AI research projects intended towards the production of other analytic tools within the next four to five years. Examples include developing of algorithms that can effectuate multilingual speech recognition & translation in noisy environments, geo-location of images without the associated meta-data, fusing of 2-D images to create 3-D models, & building tools to deduce a building’s function based on pattern-of-life analysis (Congressional Research Service, 2020). LOGISTICS. AI is envisioned to have a potential utility in the future of military logistics. The American Air Force, for instance – has begun the utilization of AI for predictive aircraft maintenance, to do away with repairs when an aircraft breaks or in concurrence with standardized fleet-wide maintenance schedules in order to suit tailored maintenance schedules for the individual aircraft. The approach is primarily used upon F-35’s Autonomic Logistics Information System, which extracts real-time sensor data embedded into the aircraft’s engines & other

2020 Handbook on Artificial Intelligence and International Law

onboard mechanisms, & feeds the data to a predictive algorithm which uses it to determine when technician inspections are needed for the aircraft (Congressional Research Service, 2020). The American Army’s Logistics Support Activity (LOGSA) has contracted IBM’s Watson, for the development of tailored maintenance schedules for the Stryker fleet based upon the information extracted from 17 sensors that were installed on each vehicle. In September of 2017, LOGSA initiated a project intended towards the utilization of Watson to analyse shipping flows for repair parts distribution, intending to calculate the most effective & efficient means of delivering supplies. The task is primarily done by human analysts, who have managed to save 100 million dollars’ worth of the Army’s budget by analyzing a meager 10% of shipping requests. Utilizing Watson, this success rate can go up to a 100% while in very short time (Congressional Research Service, 2020). CYBERSPACE OPERATIONS. AI is being presumed to be future key player in technology related to military cyber operations. In 2016, U.S. Cyber Command Admiral Michael Rogers testified that placing reliance upon human intelligence alone in cyberspace “is a losing strategy”. He later went on to clarify by the statement by stating that “If you can’t get some level of AI or machine learning with the volume of activity you’re trying to understand when you’re defending networks…you are always behind the power curve.” The cybersecurity tools in usage as of now pre-registered almost historical matches to malicious code, thereby causing hackers to become more successful by only modifying small portions of the code. AI-enabled tools on the other hand can be trained to effectively detect anomalies in broader patterns of network activities, therefore presenting a much more comprehensive & dynamic barrier to attacks (Congressional Research Service, 2020). The 2016 Cyber Grand Challenge by DARPA exhibited the potential power of AI-enabled cyber tools. The competition had challenged participants to develop algorithms that could potentially autonomously “detect, evaluate, and patch software vulnerabilities before [competing teams] have a chance to exploit them” – all to be done within a matter of seconds, rather than the usual timespan of months. This challenge was a brilliant demonstration of not only the potential speed of AI-enabled cyber tools but also the potential ability of a single algorithm to pose as a threat while defending simultaneously. The capabilities could provide for a distinct advantage in future cyber operations (Congressional Research Service, 2020). To combat deep fake technologies, the DARPA launched the Media Forensics (MediFor) project, seeking to “automatically detect manipulations, provide detailed information about how these manipulations were performed, and reason about the overall integrity of visual media.” Nevertheless, the key issue faced is that machine learning applications can be trained to outmaneuver other

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forensic tools. Therefore, the DARPA planned to host follow-on contests to ensure that forensic tools can keep up with deep-fake technologies. The DARPA also launched SemaFor, which seeks to develop algorithms that can automatically detect, attribute, & characterize numerous types of deep fakes (Congressional Research Service, 2020). SEMIAUTONOMOUS AND AUTONOMOUS VEHICLES. The American Air Force Research Lab has phase-two tested the Loyal Wingman program. The program combines an older-generation, unmanned fighter jet like an F-16 with an inhabited F-35 or F-22. In the tests, the F-16 instinctively reacted to phenomena that were not pre-programmed, such as weather & unforeseen obstacles. It’s being envisioned that the wingman program can accomplish tasks for the unmanned flight lead such as jamming electronic threats or carrying extra weapons (Congressional Research Service, 2020). In the same way, the American Marine Corps’ tested their prototypes of a vehicle that follows the soldiers or other vehicles around the battlefields in order to accomplish certain independent tasks. For instance, the Marine Corps’ Multi-Utility Tactical Transport (MUTT) is a remote-controlled, ATV-sized vehicle capable of carrying hundreds of pounds of extra equipment. Despite the system not being autonomous in the current configuration, the next batches of such technological advancements are aimed towards being autonomous (Congressional Research Service, 2020). The American Army intends to field numerous Robotic Combat Vehicles (RCVs) possessing different types of autonomous functionalities, inclusive of navigation, surveillance, & IED removal. These systems are to be deployed as “wingmen” for the Optionally Manned Fighting Vehicle (Congressional Research Service, 2020). The DARPA finished the testing of an Anti-Submarine Warfare Continuous Trail Unmanned Vessel prototype “Sea Hunter” in early 2018 before the transition of the program to the Navy. The Sea Hunter was integrated into Surface Development Squadron 1, which was tasked with overseeing “fleet familiarization, training and tactics development of [unmanned surface vessels].” The entering of Sea Hunter into service, would provide the Navy with the ability to autonomously navigate the open seas, swap out modular payloads, & coordinate missions with other unmanned vessels – all the while providing continuous submarine-hunting coverage for months at a time. Numerous analysts estimate that the Sea Hunter would cost roughly around $20,000 a day to operate, in contrast to the whopping $700,000 for a traditionally manned destroyer. Further, the Navy launched the Rapid Autonomy Integration Lab (RAIL) “to develop, test, certify and deploy new and updated autonomous capabilities (Congressional Research Service, 2020).” The DOD has been testing other AI-fueled capacities in order to enable

2020 Handbook on Artificial Intelligence and International Law

cooperative behavior, commonly known as swarming. Swarming is fundamentally a unique subset of autonomous vehicular development, derived from concepts ranging from large formations of low-cost vehicles aimed towards overwhelming defensive systems to small squadrons of vehicles that collaborate with each other to provide electronic attack, fire support, & localized navigation & communication nets for purposes of ground-troop formations. Numerous swarm capabilities are under development. For instance, in the November of 2016, the Navy finished the testing of AI-enabled swarm of five unmanned boats that patrol together a 4-by-4 mile section of the Chesapeake Bay & intercepted the “intruder” vessel. The results of this experiment have been presumed to be potentially leading to the adaptation of AI technology for the purposes of defending harbors, hunting submarines, or scouting in front of a formation of larger ships. The Navy had plans for testing swarms of underwater drones, & the Strategic Capabilities Office had successfully tested a swarm of 103 air-dropped micro-drones (Congressional Research Service, 2020). LETHAL AUTONOMOUS WEAPONS SYSTEMS (LAWS). Lethal Autonomous Weapon Systems or LAWS are a specialized modern class of weapon systems that utilize sensor suites & computer algorithms in order to independently identify targets & employ onboard weapon systems to engage & destroy the target without the manual intervening of humans. Although the systems are not yet in widespread development, they could potentially enable military operations in communications-degrade or –denied environments which cancel out the abilities or workability of traditional systems. The U.S. military does not currently have LAWS in its inventory; however, there are no legal prohibitions upon the development of LAWS (Congressional Research Service, 2020).

AI in trade negotiation; International security and autonomous weapons system; Use of Chatbots for consular affairs and public diplomacy. Coalesced with the incoming waves of incrementing interests with regards to AI in the fields of International Relations & Security Studies, the arguments pertaining to the role(s) of AI in diplomacy are also gaining velocity, despite the rather slow progression of academic investigations without clear analytical focus. The authors of a recent report have stated that a better knowledge of the relationship between AI & diplomacy would be ensured by building upon the topic of distinction between AI as a diplomatic topic, AI as a diplomatic tool & AI as a factor that shapes the environment for diplomacy to be practiced in. In the form of AI as a topic for diplomacy borders on the preconception that

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AI is relevant to a broader policy agenda ranging from economy, business, & security to democracy, human rights, & ethics. In the form of a tool for diplomacy, the aim is to understand & devise how AI is to look at ways to support the functions of diplomacy & the day-to-day tasks of diplomats. Whereas, as a factor impacting the environment of the practice of diplomacy, AI can very well turn out to be an age defining technology of our time & thereby is potentially effective enough to reshape the foundation of the international order. However it was claimed that only the second category of AI as a diplomatic tool falls under the purview of diplomatic activity, while the other two categories of AI as a policy or external driver are more relevant with regards to foreign policy thinking (Bjola, 2020). Yet another report by a German think tank called upon the Ministry of Foreign Affairs (MFAs) to immediately begin with the planning of strategies to effectively respond to the influence of AI in international affairs. Economic disruption, security & autonomous weapons, & democracy & ethics happen to be the three areas identified by them as priorities at the intersection of AI & foreign policy. The report thereby, drew a clear distinction between the foreign policy objectives & diplomatic responses; however, it fails in properly explaining the development of the latter. Although the predominant belief is that transformational changes to diplomatic institutions shall eventually be the need of the hour in meeting the challenges ahead, they favor an incremental approach to AI that builds on the successes in the short term, which, in many countries, has already been internalized in the culture of the relevant institutions, inclusive of the MFAs. In contrast to this, the authors of a report prepared for the Centre for a New American Security provided an increasingly detailed perspective as to how diplomacy & AI can assist national security objectives. E.g. AI can aid in the improvement of communications between governments & foreign publics through the lowering of language barrier amidst countries, enhancement of security of diplomatic missions through image recognition & information sorting technologies, & supporting of international humanitarian operations via monitoring of elections, assistance in peacekeeping operations, & ensuring of financial aid disbursements by ensuring that there is no misuse through anomaly detection while all the way providing a promising foundation for the purpose of examining the evolution of relationships between AI & diplomacy. The current research nevertheless, remains rather limited in the practical vision & the analytical depth. A clear expectation is there of AI demonstrating its potential as a tool for diplomacy, nevertheless, it remains unclear as to what this potential means in practical terms & how the investigation should take place theoretically. Specifically, it is of utmost importance to map the areas of diplomatic activity in which AI could actually make a substantial difference, in order to explore the nature of AI contributions, & to facilitate the understanding of the risks that they might

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entail for diplomatic work. Equally important are endeavors towards the identification of main factors that can enable or deter the adoption of AI by MFAs & towards understanding of the conditions which would cause variance in scope & intensity (Bjola, 2020). AI AS DIGITAL CONSUL ASSISTANT.

From a decisional standpoint, consular services place reliance upon highlystructured decisions, owing to them largely involving recurring & routinized operations based upon clear & stable procedures which are not in need of being treated as new – each time a decision is the made except for in critical scenarios. From a knowledge standpoint, AI-aided consular services might embody declarative & procedural knowledge for automation of routinized operations & scaffolding of human cognition via the reduction in cognitive effort. This can potentially be done with the utilization of data mining & data recovery techniques for the organization of data & the making possible the identification of patterns & relationships which would be difficult to observe otherwise. Consular services could therefore be seen as a low-hanging fruit for the integration of AI as decisions amenable to digitization, the analytical contribution further is reasonably relevant & the embodied knowledge favors the collaboration between the machine & the user. Digital platforms have come up as indispensable utilities for the managing of diplomatic crises in the digital age & for very good reasons too. They can potentially aid embassies & MFAs in comprehending the nature & gravitas of the occurrences in real-time, streamlining the decision-making process, managing of public expectations & facilitating crisis elimination. Simultaneously, the need is for them to be utilized with great care as – factual inaccuracies, coordination gaps, mismatched disclosure levels, & poor symbolic signaling could effectively derail digital efforts of crisis management (Bjola, 2020). AI AS AN EVOLVING EXECUTIVE ASSISTANT.

For the purposes of AI to be utilized in an executive assistant’s capacity, the evolution of AI machineries can potentially come about in the following stages – 1. Reactive Machines – The form of intelligence is quite basic, thus it does not possess the ability for formation of memories & cannot use past experiences for informed decision-making. The perception of the world in this case is direct, & reactive to what it ‘sees’, without possessing a representation of the world. The IBM chess program that beat Garry Kasparov in the 1990s is in fact a good example of a reactive machine. It glimpsed at the pieces on the chess board to assess

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the optimal move among various possibilities root out the most effective one, while not possessing any memories of past moves, & without any symbolic representation of chess itself (Bjola, 2020). 2.

Limited Memory – The category refers to AI systems that have the potential for using past experiences to for the purposes of informing current decisions, however these memories are transient, & as such cannot be converted into long-term experiences for the purposes of being recalled & utilized for deciding similar situations. Nevertheless, they possess pre-programmed representations of the world for guiding applications of short-term memories in decision-making. Self-driven cars utilize sensors, for instance, in order to form transient memories of instances of oncoming traffic & road conditions. These can be integrated into pre-programmed representations of road transportation for taking appropriate decision on how to navigate safely (Bjola, 2020).

3. Self-Awareness – The final stage of AI development can come about when the intelligence is finally able to understand the mental states of others, while also taking its own into consideration. In other words, when it develops consciousness. Instead of being confined by the preprogrammed settings that are able to train it for mimicking human cognitive functions, self-aware AI can learn how to think for itself about itself & the surrounding environment. This advanced form of intelligence approximates very well Bostrom’s three concepts of super intelligence (speed, collective, quality) (Bjola, 2020).

Designing AI for diplomacy: TIID Framework. It can be argued that the configurations of AI entry & exit points represent an engine that propels the succeeding generation of digital diplomacy ‘rocket ship’. Nevertheless, the fuel for feeding the proverbial rocket is to be made of specific AI models designed, built & deployed supplementary & in support of diplomatic tasks, objectives & strategies. AI modeling for diplomacy experiences natural hindrances in relation to the continuous evolution of AI technology, the difficulty therefore in converting AI models developed catering towards other areas to the rather unique field of diplomacy, or the complex issue of MFAs struggling to develop internal capacity & attract the pool of talent that would allow them to take advantage of AI opportunities (Bjola, 2020). In order to bridge this particular gap, the TIID (task, innovation, integration,

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deployment) Framework was envisioned for the purposes of designing & integrating AI solutions into the avenues of diplomatic activities & functions. The framework can be elaborated as follows: Task – Any decision in favor of supporting AI integration needs to be preceded by discussion of two related issues – firstly, as to whether the AI solution projected is for the augmentation of the capabilities of humans to deliver a task or to replace them solely, secondly, as to whether the AI solution projected would involve a data-informed approach or data-driven approach for delivering the services. In the first scenario the humans would have control of the conversion of the data into action, while in the latter scenario; the action shall be largely shaped & implemented by the AI. Chatbots used for consular services tend to be data-driven solutions that replace the work of humans. Whereas, an AI assistant would augment the capacity of a diplomat for conduction of negotiations through the providing of useful & relevant real-time information to him/her. A more nuanced position might emerge if a datadriven network of AI assistants could be monitored & supervised by humans such as in the potential case of an online public diplomacy campaign (Bjola, 2020). Innovation – The saving of time, resources & costs are the most usual drivers of innovation, however, problem framing is responsible for largely informing the scope & value of the innovative response. For instance, if, the issue of crises management is to be defined around the idea of developing an utility that can effectively provide more accurate & reliable information to the members of the embassy in times of crises, the response shall aim to design an AI solution capable enough to collect, process & interpret gigantic congregates of data in a very short time-span. However, if on the other hand, the same issue shall be regarded as a priority matter for the embassy for reaching out to nationals, the response shall focus on developing an AI utility that can effectively communicate with them. The analytical component of the innovative needs are to as well be carefully assessed (Bjola, 2020). Integration – This aspect needs to be able to provide a high value in return, especially when the pertaining to a physical-digital-physical loop. The first part (physical-digital) of the loop requires the creation of a digital record based upon the information acquired from the physical world. The second part (digitaldigital) of the loop however, refers to the usage of algorithms for the purposes of recognizing meaningful signals & patterns in the digital record. The last component (digital-physical) of the loop would make the usage of digital insights for driving action back in the physical world via real-time & informed decision making. The return value therefore is defined by the ability of the AI to effectively collect relevant information, process it insightfully, & to feed it back into the decision-making mechanism. An AI assistant designed for the purpose of countering disinformation shall not be useful, for instance, if it’s

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unable to collect data from numerous sources, identify the most relevant sources & patterns of disinformation & further design the most effective strategies for their countering (Bjola, 2020). Deployment – Once the decision has been made as to how to redesign the service, utilizing customized innovation & physical-digital integration, the question as to the suitability of the MFA institutional capacity should be considered for the deployment of the AI model. Issues concerning costs, levels of expertise, training & scale are likely to dominate considerations on the MFA side. It should nevertheless be borne in mind that it is not only the machine learning software that matters, but also its potential for integration with other new age technological advancements such as 3D printing, Internet of Things (IoT), Virtual & Augmented Reality or Cloud technologies (Bjola, 2020).

2020 Handbook on Artificial Intelligence and International Law

International Law & the Role of Actors

Abhivardhan & Dev Tejnani

Introduction This chapter is a short introduction to the role of international organizations (under public international law) and other relevant non-state actors, which come into being in the sphere of international AI governance.

Conjunction with AI The relationship between AI and International Organizations is developed by the challenges that an international organization generally addresses. This has to be addressed appropriately, considering the fact that an international organization (in this chapter, as we refer international organizations as intergovernmental organizations) has policy objectives and commitment to the sovereign governments, which are voluntary utilizing their sovereign representation cum involvement in the bodies. Now, it is suggested to treat AI under the three-tier formula as discussed in Chapter 2, (which is the Basics of International Law), i.e., AI as (1) a concept; (2) an entity; and (3) an industry. The constitutional focus and limitations of an international organization also decides how far it can go to subject to policy intervention in that particular field, which becomes the subject-matter for review and consultation. This ensures how reasonably confidence-building is achieved. This section emphasizes on the following contours related to international organizations: • • • • •

The Constitutive Scope in determining AI Policies The Consultative Role in explicating AI Policies The Adjudicatory Function in implementing AI Policies The Foresight of democratizing AI Policies The Invisible Role in Shaping AI Policies

The Constitutive Scope in determining AI Policies For an intergovernmental organization, it is always important to determine the constitutive scope that is vested within the organization to ensure that it delivers/determines the policy goals expected. In the case of AI Ethics per se,

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it would be important to assess the constitutive scope of an international organization, because of some important reasons: • Although the role of a constitution/treaty/covenant or any primary international instrument constituting that body might not matter at large, but it surely guides way for clear and distinctive policy intervention, when we would be in a stable case to democratize responsibility and confidencebuilding measures, considering the fact that the use of disruptive technologies can be prone to rapid democratization; • An international organization also establishes some basic diplomatic considerations and maneuvers to draft policies at the level of expert groups and other subsidiary inter-organs within in the intergovernmental organization; • The consultative, adjudicatory and responsible functions of the international organizations are connected with the constitutive scope of the organization; • The granting of mandates in AI policy matters would also be decided through the constitutive scope of the international organization; • The realpolitik also contributes how international organizations (IO) work, because the practical and amorphous behaviour and utility of the principle or convention of sovereignty, whether interpreted, objected, recognized or practiced in international law, shown through economic, political, strategic or information warfare/maneuvers. The constitutive role of an international organization is generally treated as a regulatory means to ensure that the core realpolitik undertakings of sovereign states remain in a reasonable conjunction5; Now, recognizing that an international organization should oversee any nuanced area of disruptive technologies, such as AI Ethics, it becomes equivalently important to see how does this fit in the three-tier model of estimation. • As a concept, there are more scientific consensuses on the idea of AI Ethics, as compared to the disagreements on the legal & policy approaches to transform and affect AI Policy among countries. Conceptual understandings would contribute in determining conjunction between AI Ethics and pure

The simplest example of the constitutive structure of an IO and its relationship with the realpolitik is the UN Security Council, where France were granted the Veto Power (or concurrent vote) along with Russia (USSR), China (Republic of), the UK and the US (France and the Origins of the United Nations, 1944–1945: "Si La France ne compte plus, qu'on nous le dise"", 2017 pp. 215–234).

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international law in line with interpreting appropriate international legal customs wherever a state could find it necessary to propose, or endorse; • As an entity, the constitutive role of international organizations might be marginalized or limited because an organization’s constitutive role might split in the following ways: ─ The organization’s role in deciding the course and legitimacy of the legal standards and principles commonly or plurally affirmed by national governments, despite cooperation with the non-state actors, and; ─ The organization’s role in putting forward critical negotiations on the nuanced and contentious issues related to disruptive technology law, diplomacy and policy, since the role can be questioned as well despite scientific relativism or clarity; The Consultative Role in explicating AI Policies Consultations effectively are important for any international organizations, because when would they do so, they would effectively contribute into some far-reaching consensuses among countries. This can be achieved through organizing conferences and summits, and creating some ‘minilateral’ groups of countries, which are involved in discussion cum negotiation. There are 2 possible stakeholders when the consultative role of an intergovernmental organization is framed – states and non-state actors such as startups/companies, NGOs, trusts, individuals and even some legitimate independent researchers, if necessary. To uphold multilateralism, the role of countries has been of much significance, but the role of non-state actors has also opened newer possibilities of a ‘multistakeholderism’ (Indian Society of Artificial Intelligence and Law, 2020 p. 1), where technology policy-making can be dealt with much wider participation of various relevant stakeholders from the academic and scientific community. Now, international organizations emanate the idea of consequentialism, but do not limit it to the level of states. The idea of multilateralism emphasizes on a global common, which can be termed as some ‘global consequentialism’, to render transition from internet governance to digital cooperation. Since the constitutive role would have to be established, and some compromises might have to be reckoned among fellow international bodies recognized by the United Nations or its organ, the Economic and Social Council, for example, the nuances and the contentious jurisdiction of the international bodies would also invite the need to explicate clear and reasonable AI policies. May it be any possible issue of concern, but it is important to understand the role of disruptive technologies with an ontological basis, and the focus should not be limited to consequentialism. The reason is that a shrewd or narrow focus

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on the theories and practices of technology-related consequentialism for some ‘common good’, would not invite better incremental policy interventions. That applies to the fact the theories on AI Ethics and even publications on AI Research have been mostly from European, American and Chinese scholars and scientists (Knight, 2019) for example. At demographic levels, the question of dominance over academic literature might be a show of merits and capacitymaking cum building, but at the same time, it is important to render autonomy state and non-state stakeholders from rest of the countries to have as much autonomy and skill enhancement to develop and conform with the global consequentialist understandings on AI Ethics and Policy. As an industry, much diversity in the usage and production of AI services and products is imminent, which would define the strategic interests of various countries in their own way. At the level of consultations, an international organization cannot avoid incremental discussions and confidence-building measures once its scientific and pragmatic role has been acknowledged cum accepted by countries to open doors for some ‘multi-stakeholderism’ of NGOs, startups and other relevant legal non-state actors. The Adjudicatory Function in implementing AI Policies At the level of implementation of AI Policies, the role of international organizations differs. Some organizations have more autonomy to intervene, while some cannot at all intervene directly or in a colorable manner, which might be detrimental to a country’s national security or sovereignty. It is important to understand that the goal of an international organization is never to further any binary political ideological objective, because despite the fact that in the theoretical aspects of international law, sovereignty is inviolable, the role of international organizations is to balance sovereignty and global connectivity. For example, the notion to have a global community, where people connect with each other and render open, legal and free relationships, is certain an objective but reasonable idea. Recognition of human rights’ universality in international law is certainly unquestionable: but the application and envisioning of standards of international human rights law cannot be lopsided or self-centered, if the vision has been acutely limited to merely target the sovereignty or the national interests of the country and its populace. In this phase, the role of AI as an industry would certainly be more significant, but yes, it must be understood clearly that the vision to adjudicate and keep a check as to how such AI policies are implemented should certainly be not myopic, because at first, it is the government and the non-state actors involved who are required to collaborate together, giving relevant primacy to the government. At national levels, the multiple stakeholders should be clear, reasonable and open to feedback. Sometimes, it is necessary that the onus of

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taking and processing the feedback should be on both the multiple and diverse stakeholders, and the government for sure. When the cycles of feedback are clearly dealt with, and demands, which are kept, are not unreasonable, or anarchic, then the ratification/implementation of that policy becomes clearly possible. It is therefore up to the multiple stakeholders such as startups, NGOs, etc., to ensure that rule of law is preserved and the economic, individual and political freedoms of individuals and groups are enhanced and protected, considering the interventionist role and behaviour of disruptive technologies like AI and the hype which is usually created most of the times by private nonstate actors such as NGOs, tech startups, firms and the rest (The Lancet Digital Health, 2020; Vishnoi, 2020; Funk, 2019). The Foresight of democratizing AI Policies The stage of foresight has more to do with economic competition and the kind of measures would be taken to ensure that multiple stakeholders, such as startups, MSMEs, NGOs, companies, think tanks and cooperative groups. Democratizing AI is a challenging issue due to some important reasons: • The question of human rights being affected by disruptive tech would always invite compliance and auditing measures for actors who are going to use AIbased products and services; • The question of cultural and collective rights would also be importantly raised, because of the fusion of the utilitarian role of artificial intelligence in ordinary lives. It might not be the issue of cultural sensitivity, or identityrelated sensitivity, but at least it can be related to the domain of dignity which every identity group (or even a multi-identity group) might ask for. However, that itself would depend a lot on the specific aspects of the policy framework and the systemic operations rendered by the agency of AI. The question of culture and identity is dealt in the chapter on international cultural law; • Since consequentialism in AI Ethics might have its own fatal setbacks as discussed before, it is suggested that the democratization of AI must be seen in a cyclic manner, with distributive and decentralized goals, which do not emphasize on the tendency to replace a status quo through external or thirdparty measures. Internal democracy is important among communities, companies and organizations, which emphasize upon short-term and longterm goals, which are not limited to myopic visions and goals like artificial general intelligence (AGI), for example, but are beyond that, based on research, academic creativity and freedom of expression to individual and collective entities. Therefore, democratizing innovation and the course of innovation in AI would also require introspection. In detail, the impact of

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education on innovation policies in AI is dealt in the first chapter of the handbook; The best an international organization can do is to ensure that: • The feedback mechanisms are globally coherent, connective and open so that stakeholders around the globe are able to connect and have a culture cum manifestation of trust and security so that flow of conversation and feedback sharing amounts to strengthening confidence-building among countries and the multiple stakeholders; • The nuances of the policies developed must be addressed along with the economic and technological advances gathered by companies and scientists, who are involved in rendering AI-related innovation, considering the fact that in the past the development of legal and policy standards has been slow in comparison with the kind of technologies that have advanced. (Castro, et al., 2019; Alford, et al., 2020) Although reaching at consensuses requires some political alignment (which might not have much to do with ideological considerations but could be related to the realpolitik), international organizations should seek the gaps between countries and the multiple stakeholders, wherever there exist some assimilation gaps. Addressing assimilation gaps, in a non-consequentialist and reasonable manner would certainly assure better and cleaner democratization of AI; The Invisible Role in Shaping AI Policies The invisible role of an international organization is generally the initiative to be led by the organizations to bridge digital cooperation among countries and the multiple stakeholders. Now, it is important to see how the bridge of cooperation, rules of negotiation cum bargaining & the status of countries and non-state actors are inherently or systemically acknowledged by the international organization. There may be some scientific consensuses, but the operative aspect, which involves how do organizations act and shape the trajectory of AI policies must be assured scrutinized to ensure ontological transparency to the inclusion of multiple stakeholders. Consequentialism must be questioned in the case of International AI Governance, because not doing it invites selective biases of scientists and innovators from a limited set of countries. International Organizations are also responsible to ensure that the information warfare behind AI Ethics, for example is also addressed, through states. Mostly, international organizations can shape AI policies through their expert groups and committees, which must be scrutinized and should not be limited by mere ideological or limited perspective goals.

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The Context of Non-State Actors It is extremely crucial to understand that the advent of the Fourth Industrial Revolution has the capacity to bring about an opportunity for the insurgents and terrorists, something which was previously only conferred upon the most powerful and the wealthy states. The advancement in the field of technology and AI will grant the non-state actors, for instance, the terrorists, insurgents, organizations which are not affiliated to the government to easily access cheap, AI powered mechanised weapons. It can enable these non-state actors to create drones, task specific artificial intelligence and also undertake activities pertaining to advanced manufacturing. It can also aid the perpetrators to create small powerful warheads, with the use of AI and Machine Learning. It is necessary to understand that failures against non-state actors can be deemed to be regarded as a fault of the country’s strategic deficiencies rather than its tactical deficiencies. The development of various technologies has the potential to scrutinize the change with regards to the political, economic and social conditions and it surely has an impact on the strategic environment of the state versus the non-state conflicts. The conundrum that arises here is that the link between these strategic conditions and technological advancements can enable non-state actors to take advantage of these various developments and therefore enable it to defeat states. The first aspect that needs to be considered is how the non-state actors have made use of AI and technology. It is imperative to throw light upon the United States and how it has dealt with the non-state actors’ use of AI and technology. The United States has taken immense efforts when it comes to pursuing nonstate actors to make use of cutting-edge technology; however, it was the nonstate actors who refused to make use of it. However, it is imperative to understand that except a few non-state actors, such as drug carters, completely rely upon technology and AI. Now, there are two reasons why the non-state actors in the United States of America refused to make use of cutting-edge technology. Firstly, the non-state actors in the United States of America were of the opinion that cutting-edge technology cannot be relied upon, the usage of which was offered to them by the government and secondly, they wanted to make use of a technology which they were comfortable in using. It is necessary to throw light upon the U.S. forces operation which took place in Iraq in the year 2003. In this security operation, which went on for five years, i.e., 20032008, the Iraqis adhered to basic household goods, for instance, mobile phones, base state phones, and garage door openers in order to make detonators for their explosives or bombs. They made use of these basic devices for a reason and the reason was that these devices were easily available and no one would suspect the usage, apart from this, these goods could be easily sold, repaired,

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opened and tampered with since they were items which were not prohibited for sale under the provisions of any law and therefore, they could tactfully make use of these gadgets in their weapons. Furthermore, commercial drones took their first flight in the year 1990s; however, the insurgents had no access to these devices until the year 2014 and 2016 (Conger, 2016). Until this time, commercial drones were a dream and they were never easily accessible or used by non-state actors. These aspects show that non-state actors have never been prone to the excessive usage of cutting-edge technology; however, with the advancement in the field of AI and technology, it is certain that technology would be available to each and every non-state actor in the civil society. Considering these aspects, it is imperative to focus upon how new technologies have the capacity for non-state actors to greatly work upon their capabilities in the immediate future. The arrival of the fourth industrial revolution has already put things in motion and therefore the advancements in technology and AI has the capacity to enable the non-state actors to make an efficient use of these technologies.

Case Studies The focus of the section on caste studies is to explain how various international organizations (intergovernmental) have formed AI policies or are contributing development and democratization of artificial intelligence and AI Ethics as a soft law. International Organizations United Nations and its Affiliate Bodies (under ECOSOC) and Groups. The most important ECOSOC-affiliated UN organization, which has been instrumenting and leading work on AI policy is the International Telecommunication Union (ITU). Organizations such as UNESCO, the World Intellectual Property Organization and the treaty body of the United Nations Convention on Certain Conventional Weapons have also contributed some good work primarily in the UN. Otherwise, these are the following bodies/groups (inexhaustive) within the United Nations, which are committed to research and policy intervention in AI: • • • •

Group of Friends on Digital Technologies at the UN Ad Hoc Expert Group on AI Ethics, UNESCO UN AI Advisory Body UNICRI Centre for Artificial Intelligence and Robotics

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• Group of Governmental Experts on LAWS • UN Development Group • UN Secretary General’s Independent Expert Advisory Group on a Data Revolution for Sustainable Development • Focus Group on Machine Learning for Future Networks including 5G • The WIPO Conversation on IP and AI • The Commission on Science and Technology for Development in UNCTAD • United Nations Global Pulse The important summits and activities led by the United Nations bodies either solely or through cooperation by states specifically on AI policy are described as follows: • • • • • •

AI for Good Global Summit The WIPO Conversation on IP and AI ITU Briefings on AI in Geneva and New York ITU GSR2018 COMEST roundtables on 11-12 September 2018 Geo-Referenced Infrastructure and Demographic Data for Development in the United Nations Population Fund • The UN World Data Forum Many or almost all of the prominent UN bodies, programmes and organizations are doing or making use of AI technologies at their best competence. Some of them require state-level cooperation, while some are merely research organizations or institute, for example, the UNICRI. Some of the organizational units employ expert groups, and are in close cooperation with countries around the globe to foster various kinds of activities. Some of these activities are described as follows: • UNESCO for example merely involves in organizing events, creating governing groups and focuses on awareness, ethics research and addressing concerns (International Telecommunication Union, 2018 pp. 28-29); • UNFPA involves Bill & Melinda Gates Foundation, DFID, Flowminder/WorldPop, Oak Ridge, National Laboratory and the Center for International Earth Science Information Network in its work on GeoReferenced Infrastructure and Demographic Data for Development (International Telecommunication Union, 2018 p. 30) in cooperation with national governments;

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• UN High Commissioner for Refugees uses predictive analytics in Somalia to predict population movements (International Telecommunication Union, 2018 p. 34); • The UNICRI Centre for Artificial Intelligence and Robotics specifically emphasizes on the Sustainable Development Goal 16, wherein they are currently researching on the criminal implications of actions and operations undertaken by AI/machines in the field of Robotics and Law (International Telecommunication Union, 2018 p. 39); • SAP SE and the United Nations Industrial Development Organization (UNIDO) closely are working on the Sustainable Development Goals, and even with the Russian Federation, UNIDO is emphasizing work on converging disruptive technologies along with AI through organizing forums (International Telecommunication Union, 2018 p. 42); • UNITAR is also involved in geospatial tech and satellite imagery research and infusing the use of AI through UNOSAT (International Telecommunication Union, 2018 p. 46); European Union, NATO and the Council of Europe. The European Union and the Council of Europe usually act in different means in some of the other ways possible, because while the EU is mostly focused on rule of law, governance and other interdisciplinary challenges of diplomatic and local cooperation, majorly, the Council of Europe with the European Court of Human Rights, specifically emphasizes on international human rights law & protecting cum preserving the European way of human rights governance. NATO is a defence-centric strategic organization, which showers the transatlantic relationship between the United States of America and the Council of Europe member-states (except Russia and obvious antagonistic countries). Here is a special focus on NATO’s work on AI Ethics and International Law: • NATO allies have participated in the GGE on Lethal Autonomous Weapons under the UN and have developed the 11 guiding principles; • NATO believes in dynamic adoption and responsible use (Christie, 2020) of artificial intelligence; • NATO Cooperation Cyber Defense Centre for Excellence is working on the relationship between cyber operations and international law. Under Micheal N Schmitt, the two Tallinn Manuals on the International Law Applicable to Cyber Operations was published recently; The European Union is involved in extensive work in the field of artificial intelligence and law, policy & international affairs. However, the emphasis is

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mostly on state or global consequentialism. However, the European Parliament and the European Commission are involved in some principled legal avenues in the field of AI and Law. Some of the important works on AI Ethics by the European Union is described as follows: • On 8 April 2019, the High-Level Expert Group on AI presented Ethics Guidelines for Trustworthy Artificial Intelligence; • In February 2020, the EU published an important policy paper on the liability framework on AI systems and services, including on the life cycles of ML systems; • European Union using the 2018 General Data Protection Regulation clearly focuses on its digital market initiative, where protecting user privacy and data flow is their strictest of interest; • EU has developed various policy papers and reports on AI ethics, but most of them are central to the European single market. One of the recent notable papers published by Europe is on AI and civil liability in 2020 (Alford, et al., 2020); The Council of Europe’s work on artificial intelligence is clearly on regulating the development cum regulation of artificial intelligence, and the dimension of human rights accorded with AI. Some of the important works on AI Ethics as a soft law by the Council of Europe is enumerated as follows: • The Council has established an Ad hoc Committee on Artificial Intelligence (CAHAI), under the authority of the Committee of Ministers, which simply emphasizes on human rights, democracy and rule of law, with respect to the implications of artificial intelligence (Council of Europe, 2020); • CAHAI focuses on the design, development and regulation of AI and opens room for various multi-stakeholder consultations; • The Council of Europe has recognized the importance of systemic and operational algorithms in technology law, and even published a set of recommendations on the human rights impacts of algorithmic systems (Council of Europe, 2020); • CAHAI has also adopted a feasibility study on a legal framework on AI design, development and application on 17 December 2020;

African Union. The African Union (AU) in their third Specialised Technical Committee (STCCICT3) meeting among their ministers, acknowledged the role of digital technologies and innovation for the African continent (African Union, 2019). In 2018, UNESCO organized a Forum for Artificial Intelligence in Africa in

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Morocco. Following the same, the African Union Working Group on Artificial Intelligence organized their first cohort in 2019 in Cairo, Egypt, focusing on the elementary aspects of developing and focusing on AI as an industry (Data Guidance, 2019). Apart from the UNESCO’s 2018 Conference, there is currently not much clarity of the African Union as a whole to develop more multi-stakeholder discussions and so forth. Except few countries such as Egypt, not many African countries have any AI strategies and even due to a much lack of resources, a maturation in Africa’s strategy is still awaited. Non-State Actors

Small Warheads. The development of AI has brought about a multitude of changes when it comes to increasing the power which is conferred upon in the various warheads. The most efficient of the warheads are bolstering and working towards the motto which specifically enumerates upon, “bringing the detonator, and not the explosive.” • From the year 2015, donning a span of two years, that is, until 2017, a number of Russian operatives or individuals deemed to be regarded as the Ukrainian separatists have made the use of drones in order to drop grenades, now these grenades were made out of thermite and it lead to the occurrence of innumerable attacks on the Ukrainian Government ammunition dump which lead to tonnes of explosives being detonated in a split second (Mizokami, 2017). • A small warhead could be made a potentially hazardous and extremely harmful explosive by the usage of an explosively shaped penetrator (EFP). The EFP could be deemed to be regarded as a device which possesses dimensions of approximately 1 inch in its diameter, containing as little as 1 ounce of high explosive, which could have the capacity to penetrate up to ½ inch of steel. This device is a small device which can easily be mounted on a range of drones and could potentially act as detonators. This is the development in the field of AI and technology which can enable non-state actors, such as terrorists, insurgents, private corporations manufacturing drones and other such small devices. The EFP also can be deemed to be regarded as a tool which was used to detonate commercial fuel trucks which were extremely essential when it came to the US operations which were undertaken in Afghanistan and Iraq. These small devices are so powerful that if they are simply launched at the hood of a moving vehicle, it could destroy the entire vehicle within seconds, plus they are automated and work on an algorithm which can be launched at any point and a potential attack could just place in a split second.

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• In fact, in the year 2013, GoPro Cameras were used in order to film individual truck drives indulging in off-roading races. Similarly, GoPro is a device which can be used in order to create a warhead, by simply attaching a GoPro to an EFP and this can potentially enable the individual handling the automated device to aim and shoot the EFP wherever the lens of the camera is focusing upon. An operator of such an EFP device with the use of GoPro can perhaps even shoot a vehicle and take it down in seconds regardless of the vehicle moving in traffic. This shows the use of other devices provided by non-state actors which also aid in the creation of potentially harmful detonating devices or in the creation of hazardous weapons. All of this is possible due to the immense advancements in the field of technology and AI, which enables the user of such devices to simply code the EFP and launch it at any point, making sure that the aim is precise. • It is imperative to note that EFPs are majorly used in Iraq and the use of Drones and GoPro cameras has the potential to actively look for the designated target on the basis of the algorithms on which it runs and these weapons are so powerful that it can perhaps also lead to a blast. It is extremely crucial to understand that potential warheads can be created with multiple individuals acting in the capacity of penetrators (Fong, et al.) and self-forging fins (Liu, et al., 2014).

Advanced Manufacturing. This will lead to the manufacturing of innumerable drones, which would be small, compact, inexpensive yet effective. Advanced Manufacturing takes under its scope the aspects with regards to additive manufacturing, robots and completed makes a use of Artificial Intelligence in order to rapidly increase the efficiency and the speed when it comes to manufacturing. Since the last ten years, 3D pioneers have focused and grasped the necessary skills with regards to printing and they have focused majorly on small drone production and how it requires quick responsiveness when it comes to printing composite material. • In the year 2016, “Carbon”, launched a 3D printer, specifically designed for commercial use which was deemed to be regarded as 100 times faster than all the previous printers. Apart from this, the growth with regards to 3D printing has grown immensely and has led to the development of both quality and the way in which the products are manufactured thereby reducing or cutting down on costs. In fact, a popular website elucidates upon the rates of top 10 3D printed drone kits which were made available for sale commercially (3Dnatives, 2018);

Drones. Since there has been a massive increase in the 3D printing speeds, it can be said

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that the same can have major implications on warfare. In the year 2014, a number of experts at the University of Virginia were able to produce and print a 3D drone in a day’s time. They snapped an electrical motor, used two batteries and used an Android Cell Phone in order to make an autonomous AI augmented drone which had a range of 50 kilometres. The entire cost of printing and the assembly of the drone, approximately costed somewhere around $800 (Golson, 2014). The same had the capacity to be controlled on the ground level, however, the Geo Positioning Satellite (GPS) of the Android Phone permitted the drone to take off and fly at a specific route only. However, this flight was AI powered, i.e., autonomous. However, it is imperative to understand that such a system has the capacity to be rendered vulnerable due to GPS jamming, however, a lot of organizations and experts are bolstering their resources towards developing an algorithm which can enable an individual to develop drones which would render it possible from drones to be navigated within the environments which have been restricted by the GPS (Bauer, et al., 2014). • There are certain programs that have been designed in a way which makes use of cell phone cameras when it comes to identifying individuals and objects, living and non-living, even if there isn’t proper lighting, i.e., it has the capacity to identify objects even in low-light conditions (Grigonis, 2017). It can be concluded from this point that small warheads can be easily assembled. The requirements are quite simple. To create an autonomous AI based drone, which can have the potential to cause a mass destruction, requires a GPS navigation system, a cell phone, target identification and one can easily create a cheap drone which may possess the capacity to reach and range a dozen of miles. • Long range air (Aerovel, 2014) and autonomous drones having the capacity to work under the sea (Thompson, 2013) are being manufactured and the individuals who are making these drones are non-state actors who are finding it extremely difficult to cut down on their costs and increase their payload. It is imperative to throw light upon the Aerovel Flex rotor Drone which has the capacity to travel 1,500 miles, the Defiant Lab DX-3 has a capacity to travel and cover 900 miles (Aerovel, 2017), the Volans-I, having a capacity to travel and cover 500 miles whilst holding a 20-pound payload and maintaining a constant speed of 150 miles per hour (Kolodny, et al., 2018). The smaller versions of these devices may not be as powerful as these It is imperative to throw light upon the Aerovel Flex rotor Drone which has the capacity to travel 1,500 miles, the Defiant Lab DX-3 has a capacity to travel and cover 900 miles, the Volans-I, having a capacity to travel and cover 500 miles whilst holding a 20-pound payload and maintaining a constant speed of 150 miles per hour. The smaller versions of these devices may not be as

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powerful as these devices are, however, they do possess the radar area of a small bird (Rogoway, 2016). It is imperative to understand that just like how other devices can be easily improved for a very cheap price, similarly these devices can also be updated and used for a relatively cheap price and this could be deemed to be regarded as a potential threat to the naval and the air forces and their bases could be open to potential vulnerabilities.

Task-Specific Artificial Intelligence. • The development of general artificial intelligence is something which is being thoroughly deliberated upon and there is no surety with regards to when or how will general artificial intelligence emerge. However, task-specific artificial intelligence is something which is growing and developing. It can still be deemed to be regarded as a niche area however; it has certain aspects which make it distinctive and feasible. ─ For AI to be deemed to be regarded as task-specific, it is imperative to understand that the drones which are made for attacking, or carrying out autonomous attacks, come across two major issues, i.e. navigation and target identification. For instance, the Israeli Harop drone which was first launched in 2005, had the capacity to use GPS and it could travel by itself reaching the desired target area as per programmed by the algorithm. It also had the capacity to visualize infra-red radiations and lights and had an electronic search mode option which enabled it to examine and potentially mark its target (Airforce Technology); ─ Visualizing and marking the target object has been deemed to be regarded as a separate issue and it ensures that autonomous systems are put to use when it comes to analyzing a specified target and then maneuvering the system through an obstacle when the right time occurs. This can be deemed to be regarded as a very challenging issue; however, a number of commercial firms have been bolstering their resources towards making their devices autonomous. All these commercial organizations are nonstate actors and they provide effective, precise and inexpensive sensors and manufacture these drones at a very less price, enabling insurgents and a number of other countries with barbaric intentions to benefit and thoroughly make use of AI. It is quite necessary to throw light upon the fact that as of January 2019, a number of commercial firms have been bolstering towards creating an ecosystem wherein they can offer 9 different models of drones which could have the capability to work autonomously and follow and film an athlete. Apart from that, it was also being built in order to ensure that mountain bike riders who are going on riding trails are being watched over upon (Wong, 2019);

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Section 3: AI & Digital Studies

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International Law and Technology

Sameer Samal

Introduction Artificial Intelligence and its other subsets are integrating into our lives at a great pace. They leave a deep impact and have potential benefits for businesses, citizens, and governments throughout the world. Considering the pace at which these technologies are developing, regulators and policy makers are struggling to catch-up and take control. This chapter deals with the nuances of international law and technology, more particularly Artificial Intelligence and its related technologies. The Universal Declaration of Human Rights and the International Covenant on Civil and Political Rights have furthered certain essential human and civil rights that every individual regardless of any of the protected status is entitled for. Considering the progress that the modern society has made since the inception of these landmark core fundamental rights instruments, the technological advancements have also made a substantial impact on the human and civil rights framework, whether international or municipal. Technology, and in this case Artificial Intelligence specifically, can assist in safeguarding and also developing the human rights agenda. Some examples of this include the use of forensic sciences to appropriately hold perpetrators accountable, gather information on abuse of rights with the assistance of image recognition, use of satellite systems to identify and monitor the flow and movement of displaced people. Similar to other fields, even the use of technology has the capacity to undermine the efforts to safeguard individual rights. The use of unregulated surveillance tools and technologies by governments, increase in political “deep fakes”, and the use of AI to destabilize democracies are a few such examples of unhindered and unregulated demerits of the use of AI (Harvard Kennedy School- Carr Center for Human Rights Policy). The ethical and legal aspects of AI include the issues of privacy protection, accountability and liability, freedom of speech and expression, nondiscrimination, autonomous AI-powered systems, among others. The legal concerns of Artificial Intelligence include copyright related issues, personal data protection and privacy issues, and other consumer protection aspects (Stankovic, et al., 2017).

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Artificial Intelligence is also being used in the healthcare sector in a range of applications. It is used for imaging and diagnostics for clinical purposes, and management tools for hospital management. It may also be applicable in patient-doctor relationship as well as for surgeries. Therefore, it is necessary to incorporate the principles of informed consent before any clinical data is collected. Artificial Intelligence powered chatbots and mobile apps are being increasingly used that range from diet recommending apps to exercise generators. Such apps raise the question of data collection and usage. Most of the times, users ignore the privacy agreement and blindly agree to all the terms and conditions provided therein (Ethical and Legal Challenges of Artificial Intelligence-Driven Healthcare, 2020). Therefore, a way to tackle these issues of safeguarding user privacy while paving the way for technology development is necessary. Artificial Intelligence intersects with diplomacy at two levels, (i) AI as a diplomatic tool; and (ii) AI as a topic for diplomacy. The use of Artificial Intelligence as a diplomatic tool is necessary and inevitable as human resources cannot match the capacity of such advanced technologies. The speed and wideranged application is out of reach of normal human capacity. Therefore, Artificial Intelligence can assist humans in diplomatic functions (Samal, et al., 2020). If shared or publicly available, AI tools can substantially contribute to leveling of the playing field at international negotiation tables. For example, The Cognitive Trade Advisor (CTA), a software designed to support diplomats in preparing for international trade negotiations, was launched at the 2018 World Trade Organization (WTO) Public Forum (Hone, et al., 2020). Bilateral and multilateral engagement with like-minded allies to exchange views on these issues combined with on-the-ground analysis with the goal of better policy decisions is a beneficial approach for the integration of AI, especially for developing countries who might face challenges in developing AI as a tool on their own (Bjola, 2020).

Legal Background Blockchain Technology and International Law Certain points under this section such as, blockchain based legal registries, and blockchain and data protection will be covered. Throughout nations across the globe the status, rights, and identity of persons and other legal entities are recorded in registers for legal compliance purposes. The purpose is to ensure secure evidence that can be sufficiently presented before any adjudicating

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authority. The same applies for blockchains. Screenshots of websites or any other digitalized content, and contracts, whether smart or not, are required. The second aspect of this section is, blockchain and data protection. The safeguarding measures of blockchains are so strong and complex that it is technically impossible to erase information. Considering the provisions of data protection laws, such as the GDPR which requires the personal data of individuals must be stored only for lawful purpose and only till it serves the purpose, and the concerned individual must have access to the information at all times, the blockchain technology can prove to be incompliant with such provisions. Therefore, it is necessary to develop the blockchain infrastructure in such a manner that it complies with certain inviolable principles of basic privacy laws (Brunner, 2020). The United Nations Commission on International Trade Law (hereinafter referred to as UNCITRAL for the sake of brevity) is working towards the concept of technology neutrality. This concept requires that the law should not require the use of specific technology for interacting and storing information electronically. This ensures that law is capable of accommodating contemporary and future technological developments. These works are under the Model Law on Electronic Commerce (1996), the Model Law on Electronic Signatures (2001), and the Convention on the Use of Electronic Communications in International Contracts (2005). Defense Technology and International Law Certain countries with advanced militaries are already capable of automating many aspects of military and security forces. It ranges from equipment maintenance and personnel systems to the use and deployment of autonomous weapons such as robots and drones. These advanced weapons have come to compliance with principles of the laws of armed conflict and humanitarian laws and include the provisions to hold states and individuals accountable for any acts or omissions that violate human rights and civil protection of individuals. These developments are causing a global debate on the procurement and development of such weapon systems that have the capacity to autonomously deploy as well as target lethal force which would be beyond the control of humans (How can International Law Regulate Autonomous Weapons?, 2018). The questions that are required to be resolved under this aspect are of accountability and non-discrimination. When autonomous weapon systems capable of deploying and targeting without any human control are used in modern warfare and armed conflicts, the major concern is that of accountability. Machines cannot be held accountable under any international law principles and neither under specific provisions of the International

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Humanitarian Law nor under the guiding principles of the law of armed conflict. Therefore, a certain mechanism to hold individuals and the concerned state accountable for any wrongdoings or violations of human rights and other civil rights principles of humans is necessary. The second concern is that of non-discrimination. When most of the advanced AI systems are proved to be biased, how will these weapon systems prove to be non-discriminatory is a question that needs to be resolved with open and supportive dialogues by experts and stakeholders. United Nations and its approach towards technology United Nations has been working actively towards the safe incorporation of Artificial Intelligence and other advanced technologies in our society. The organization has been working towards the following fields: • Comprehensive Nuclear-Test-Ban Treaty Organization: ─ The international Monitoring System will consist of 337 facilities throughout the world to monitor the planet for signs of nuclear explosions due to weapons testing on the earth’s surface, underground and underwater. • The Food and Agriculture Organization: • Detection of fall army worm damage using mobile applications. ─ Port inspectors, customs agents, fish traders, and other users without formal taxonomic training. ─ Land and crop classification using satellite images and ground reference data. ─ Fleet estimation for improving fisheries statistics. ─ Palm tree mapping from satellite images. ─ Fish species identification ─ Fishing gear identification ─ Aquaculture mapping • International Civil Aviation Organization: ─ Project NORM: deep learning model on critically assessment of notices to airman. ─ Project AIWP: natural language processing of working papers for 40th ICAO assembly to automatically summarize working papers. ─ Project Long-landing Detection: recurrent neural network for the prediction of long landings using radar positions. • International Labour Organization: ─ The impact on jobs and employment

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─ The economics of artificial intelligence and the implications for the future of work ─ Distributional consequences of AI and its policy response ─ Skill development and strategies for future labour markets International Maritime Organization: ─ Shipping digitalization and cooperation with ports. ─ Maritime single window. ─ Maritime autonomous surface ships ─ E-navigation ─ Marine environmental protection and AI ─ Digital Review International Organization for Migration: ─ Humanitarian data science and ethics groups ─ Application of techniques for internal quality control within displacement tracking matrix ─ Global migration data analysis center International Telecommunication Union: ─ The AI for Good Global Summit ─ Focus Group on AI and Health ─ Focus group on machine learning for future networks including 5G ─ Focus groups on environmental efficiency for AI and other emerging technologies ─ The ITU Kaleidoscope ─ ITU Publications ─ The world telecommunication/ ICT Policy Forum United Nations Program on HIV/AIDS: ─ Meet Marlo ─ Health Innovation Exchange and TimBre United Nations Conference on Trade and Development: ─ United Nations Commission on Science and Technology for Development ─ Technology and Innovation Report ─ Interagency Coordination on STI for Sustainable Goals Development United Nations Department of Economic and Social Affairs: ─ E-Government Survey ─ ICEGOV Australia 2019 United Nations Department of Political and Peacebuilding Affairs and Department of Peace Operations: ─ Toolkit on Digital Technologies and Mediation in Armed Conflicts

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─ Sentiment Analysis and Digital Focus Groups • United Nations Economic Commission for Europe: ─ Autonomous Vehicles ─ The united for smart sustainable cities initiative ─ Application of machine learning to the production of official statistics ─ Trade Facilitation • United Nations Environment Program: ─ Water related ecosystems ─ Funding analysis and prediction platform • United Nations Education, Scientific and Cultural Organization: ─ Setting ethical norms and standards ─ Positioning UNESCO as a platform for international intellectual debate ─ Fostering policy dialogue ─ Capacity building Cybersecurity in International Law Cybersecurity and Cybercrimes are one of the most important aspects of international law but are agreeably the most neglected areas. It is partially because cybercrimes activities are not uniformly defined under international law, but also in part because states have not effectively agreed on the traditional crimes. An ambiguity in the definition of cybercrime activities has resulted in scarce policies on appropriate scale for regulation as well as regularization of cybercrime activities. However, with the advent of advanced technologies such as Artificial Intelligence and its subsets, an urgent need for a secure internet space with sufficient governance is felt. The Budapest Convention on Cybercrime. The Budapest Convention on Cybercrime is the first international treaty on criminal activities through the internet and other computer networks. The treaty is signed by the member states of the Council of Europe and other States that are additional signatories to it. It particularly deals with issues of copyright infringement, child pornography, computer-related frauds, and network security violations. The purpose of this treaty is to pursue a uniform criminal policy aimed at the protection of society against activities such as cybercrimes. The objective is aimed to be achieved by adopting appropriate legislation and enabling international co-operation (Council of Europe, 2001). The Convention deals with the aforementioned topics under the following sections: • Substantive criminal law that provides for provisions relating to the offences against the confidentiality, integrity and availability of computer data and systems, and other

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computer and content-related offences, as well as infringements of copyrights and other related rights. • Procedural law that mentions common provisions regarding the scope of procedural provisions and other safeguards. This section also deals with the expedited preservation of stored computer data, real-time collection of computer data, and search and seizure of computer systems. • Jurisdiction over offences mentioned in Articles 2 to 11 of the Convention. • General Principles relating to international co-operation such as the principles relating to extradition, mutual assistance between the signatory parties for investigation and other related purposes.

An Additional Protocol to the Convention on Cybercrime was adopted on 28th January, 2003 that deals with concerns relating to the criminalization of acts of a racist and xenophobic nature through computer systems. Applicability of International Law on Cyber Warfare. The Tallinn Manual on the International Law Applicable to Cyber Warfare has attempted to define cyber-attack as, ‘a cyber operation, whether offensive or defensive that is reasonably expected to cause injury or death to persons or damage or destruction to objects’ (International Group of Experts, 2013). The initial question to consider before any propositions are introduced in this section is whether the extant international laws are applicable to cyber issues at all, if yes, how. The discussion on this subject varies from a full application of the Law of Armed Conflicts as per the pronouncement of the International Court of Justice that it applies to, ‘any use of force, regardless of the weapons employed’, to a more-narrow view of the Permanent Court of Justice that acts not forbidden under international law are generally considered to be permitted. Currently the sovereignty, jurisdiction and control over cyberspace is under the purview of those respective States. The uniform principle relating to the sovereign control over cyber infrastructure located in international airspace, high seas, or outer space is usually subject to the jurisdiction of the State of registration. Therefore, the principles of sovereign immunity and inviolability are long-established, and any practice contrary to this principle such as any interference with the cyber infrastructure constitutes a violation of the sovereignty of the respective State. The general principle relating to the prohibition of the use of force under international law includes, but not limited to, that any cyber operation constituting a threat to the political or territorial integrity of a sovereign State is considered unlawful. In response to any cyber-attack, a target State may exercise its inherent right of self-defense. However, it is necessary to ensure that the necessity as well as the proportionality of the self-defense must be appropriate (International Group of Experts, 2013).

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International governmental organizations also play a crucial role in the governance of cybercrimes and cybersecurity issues. The United Nations Charter, under Article 39, empowers the United Nations Security Council to, ‘determine the existence of any threat to the peace, breach of peace, or act of aggression and make recommendations or decide what measures shall be taken in accordance with Articles 41 and 42, to maintain or restore international peace and security’. Therefore, it can be safely inferred that, if the UN Security Council determines any cyber operation to be under the purview of the aforementioned provision, it may authorize non-forceful measures as well as forceful measures if deemed necessary. International Cyber Norms. The inherent nature of technology has been elucidated by Albert Einstein, although in a pessimistic manner, it is still relevant today. His notion, ‘all our lauded technological progress – our very civilization – is like the axe in the hand of the pathological criminal’, meant that the opportunities that technologies present to mankind also bring certain vulnerabilities with them. The rapid advancement of cyberspace has made it comparatively challenging to regulate and limit malevolent activities (Osula, et al., 2016). Certain prominent multilateral initiatives that limit state activities in cyberspace are: • Organization for Security and Co-operation in Europe (OSCE) and other Confidence-Building Measures. • The Shanghai Cooperation Organization and International Information Security. • The North Atlantic Treaty Organization. • The G20 Antalya Summit. • Measures by the Council of the European Union. The activities concerning the international community’s legal architecture including the cyberspace is dealt in Article 38 of the Statute of the International Court of Justice, which is as follows: “1. The Court, whose function is to decide in accordance with international law such disputes as are submitted to it, shall apply: a. International conventions, whether general or particular, establishing rules expressly recognized by the contesting states; b. international custom, as evidence of a general practice accepted as law; c. the general principles of law recognized by civilized nations;

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d. subject to the provisions of Article 59, judicial decisions and the teaching of the most highly qualified publicists of the various nations, as subsidiary means for the determination of rules of law. 2. This provision shall not prejudice the power of the Court to decide a case ex aequo et bono, if the parties agree thereto.” International legal norms are considered different from the domestic as well as inter-state regulations governing cyberspace under the ground that in the event of non-compliance international legal responsibility and accountability may arise (United Nations, 2001). Traditionally, these international legal norms were agreed to be binding only on the States, the application of which on the domestic organizations of the States were left to the decision of those respective States.

Conjunction with Artificial Intelligence Disruption of Status Quo by Technology Technology is not the key consideration in managing digital transformations, including that of AI. It is agreed that technology is integral in driving the process of digital transformation but the success or the failure of the same lies in the individuals and the human workforce facilitating it. The augmentation of workforce with data driven technologies can cause to release a human capital in the digital era (Bianculli, 2017). Apart from the considerations of employment and workforce analytics, other factors are at stake that are affected by the disruption caused by Artificial Intelligence. The reason for an increased use of AI throughout industries is the increased processing power that makes execution of complex and at times inhuman tasks quickly and more effectively. It is expected that by the next decade there will be an exponential increase in the number of commercial AI based applications. The applications range from the use of AI in basic functions such as video streaming recommendations to advanced machinery for supporting automobile and the aviation industries (Sallomi, 2015). Certain industries to which AI might cause the most disruption are (Bloch, 2019): • • • •

Hiring and employee development Industry Investments and Insurance Industry Smart Home Technology Industry Healthcare Industry

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Apart from the afore-mentioned four industries there are certain other industries that will be substantially affected due to AI, these are (Roe, 2018): • • • • • • • • •

Agriculture Industry Call Centers Industry Customer Experience Industry Energy and Mining Industry Intellectual Property IT Service Management Industry Manufacturing Industry Technical Support Industry Software Development Industry

Geopolitics and AI The global perspective about the potential of AI to create and promote misinformation is evident but AI also has the potential to detect misinformation and limit its spread. This dual perspective is essential in the context of geopolitics as it is closely related to how governments and institutions shape and also react to public opinion about AI and such similar technologies. It is expected that over the next decade Artificial Intelligence will experience a substantial growth and advancement in the digital information ecosystem. Additionally, during this period, the AI based technologies and their methods will turn into complex and sophisticated techniques for spreading misinformation and sponsoring propaganda. This will impact geopolitics in multiple and important ways. In countries that promote authoritarian activities such as China and Russia, the governments will have to exercise high level of surveillance over information through government sponsored surveillance activities as well as private censorship. Artificial Intelligence might also be used to alter and influence state elections by the spreading of misinformation, the same method might also be used to influence democratic elections in other states (Rugge, 2019). Another aspect of geopolitics relating to AI is cyber-colonialism (The geopolitics of artificial intelligence: The return of empires?, 2018). A report submitted by Cedric Villani, a mathematician and parliamentarian charged with a fact-finding mission on AI, stated, “There is a danger of a capture of value and competency by foreign institutions. To some extent, this has already taken place in France: the big platforms are the French government’s number one candidates for the development of artificial intelligence.” He further went on to explain, “These big platforms capture all the added value: the value of the

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brains they recruit, and that of the applications and services, by the data that they absorb. The word is very brutal, but technically it is a colonial kind of procedure: you exploit a local resource by setting up a system that attracts the value added to your economy. That is what is called cyber-colonization.” (Belot, 2018) The future course of geopolitics and AI is identified as follows (Pauwels, 2018): • AI and the degradation of truth: the capacity of spreading misinformation and development of sophisticated technologies such as “Deepfakes”. • AI and precision surveillance: the training and development of AI systems that have the capabilities to predict massive data sets ranging from a various aspect of our lives, such as financial markets, traffic patterns, consumer behavior, health records, etc. • Cyber-race and Battlefield AI • Global governance of AI Policy Approaches on Responsible AI The three main pillars for establishing a successful base for a policy framework regarding Responsible AI are (Responsible Artificial Intelligence: Desiging for Human Values, 2017): • Accountability • Responsibility • Transparency When a semi-autonomous or an entirely autonomous AI system is deployed in real life scenarios the main and the first concern that comes to the mind of any ration person is, ‘what if something goes wrong?’. This question needs to be dealt with the pillar of accountability. For instance, who will be held accountable if a self-driving vehicle harms a pedestrian? Therefore, the parameter and mechanism of identifying and holding an individual or a group of individuals before such systems are deployed are necessary. Secondly, considering that AI has reached a transition point where the need for mechanism to enable the AI systems to themselves reason and act in accordance to the basic principles of reasonability and in terms of human ethics and values, it is necessary to develop technical aspects regarding responsibility. Thirdly, transparency is necessary for an AI system to be understood and developed. Certain AI technologies are considered as ‘black-box models’ due to the difficulty in understanding them. Artificial Intelligence and its subsets

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have the power to transform the human civilisation by improving our autonomy and wellbeing. However, to effectively interact with this technology, it is essential for users to trust it. As stated earlier, trust can be built by sharing adequate information about the technology, its process and all other associated details. While the early models of artificial intelligence were easily understandable and interpretable by humans, the last few years have witnessed the significant growth of opaque or black-box models. A black-box machine learning (ML) model contains thousands of parameters and hundreds of layers that render these models practically impossible to understand. These models are being increasingly used in making important predictions in critical context (Explainable Artificial Intelligenc (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI, 2020). Therefore, all the affected stakeholders have begun demanding more transparency to understand these models better. Explanation about the output from the model is crucial in some fields, such as healthcare, where a life is at stake and the slightest error in understanding the prediction from the model can have serious repercussions. Therefore, it is crucial for these models to be understandable, so that the decisions based on the output can be justifiable.

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Fig. 1. The ART Principles. Image Credit: Virginia Dignum, Delft University of Technology, The Netherlands

Case Studies A Broad View (The Wider Realm to Artificial Intelligene in International Law, 2018) This section specifically deals with the nuances of Artificial Intelligence and its broad scope under International Law. Considering the nature of this chapter, it is difficult to cover all the aspects of AI and International Law. Therefore, attempt has been made to summarize and express the views mentioned under the referred article, “The Wider Realm to Artificial Intelligence in International Law”. Artificial Intelligence has the potential to provide a broad utility to the applied areas of International Law. Some of these areas of application where

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AI does have a significant role, are: • Global Health Law • Customary International Law • Statehood and Federalism In the words of the author, “Global Health Law ascribes more than a mere reflective action under globalization and state responsibilities and intervention of International Organizations.” Referring to Lawrence Gostin, “Global health law is a field that encompasses the legal norms, processes, and institutions needed to create the conditions for people throughout the world to attain the highest possible level of physical and mental health”. Similar to other aspects of international law, even under contemporary global health governance, states have been hesitant to create international instruments of binding force that may provide meaningful support, funding, and other incentives for the protection of citizens from poorer countries. The establishment of international legal norms and functional institutions to develop a shared humanitarian vision on global health governance is necessary. The International Court of Justice in Hungary v. Slovakia, 1997 I.C.J. 7 (April 9), made an important consideration and analyzed a statement submitted by Slovakia pertaining to a Variant C, “It is general principle of international law that a party injured by the performance of another contract party must seek to mitigate the damage he has sustained”, “while this principle might thus provide a basis for the calculation of damages, it could not, on the other hand, justify an otherwise”. In this respect, the author has proposed that, “AI can advise and control the aspects of state practices where it can become a separate alterative to the sovereign’s application of mind provided that the political essence existent in the society does not due and is left to replenish itself with time.” Conclusively, it is proposed that states must “regulate and entrepreneur the subjectivity of international law and not primarily reduce to become a subjective of this narrative and directive.”

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International Privacy Law

Sameer Samal and Saakshi Agarwal

Introduction The right to privacy is perhaps the most discussed concern in Artificial Intelligence systems. Its interaction with various aspects of privacy and their impact on policy is necessary considering the growth in its use. However, the impact of Artificial Intelligence and its subsets on the right to privacy on a global scale is not a much-discussed subject. This chapter deals with the nuances of international privacy law and its conjunction with advanced technologies such as Artificial Intelligence. The right to privacy is not limited to data privacy but goes beyond it. To understand the nature of privacy and its conjunction with Artificial Intelligence, it is necessary to elucidate the jurisprudence of privacy and its history. This chapter has covered certain aspects of privacy jurisprudence and the nature of the right to privacy under international law. The concept of privacy and its relationship with and the balance with national security has also been discussed. The ideals of legal regulation and regularization has been briefly elaborated in the legal introduction section. The most important section, along with the legal introduction, is the conjunction of privacy with artificial intelligence. Therefore, the authors have tried to cover diverse aspects of AI and its interaction with privacy. The basics of Artificial Intelligence and privacy, data-algorithms and privacy, Artificial Intelligence as a legal entity, the imposition of civil liability upon Artificial Intelligence systems, and Machine Learning and the role of privacy. To elaborate on the concept of individual privacy and national security dynamics, a case study has been discussed.

Legal Background Privacy Jurisprudence and its History The concept of privacy is a wide subject that spreads over a multitude of fields. It is regularly used in legal, political and philosophical discussions. Therefore, it is difficult to compartmentalize and define the concept of privacy precisely. Even if attempts are made to define the concept, it will definitely contain certain flaws. The notion of privacy, although not entirely distinct, is unique to every

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sociological sphere. It has its roots in the anthropological and sociological discussions about its inception in various cultures (DeCew, 2018). The historical perspective to understand the concept of privacy cannot be completed without the reference to Aristotle’s distinction between the private and public spheres of an individual’s life. The public sphere, also known as polis, refers to the political involvements, while the personal sphere, also known as oikos, refers to the individual’s domestic and family life. Various other commentators, including John Stuart Mill and John Locke, have discussed this distinction in their essay (Mill, 1859) and treatise (Locke, 1689), respectively. There are various views taken by various theorists about the philosophical nature of privacy. These views range from a mere right to control the personal information about oneself (Parent, 1983) to more wider connotations that privacy is an essential concept for the protection of human dignity (Bloustein, 1964); the view that even the Hon’ble Supreme Court of India had held in its landmark ruling (Supreme Court of India, 2017). The right to privacy is also a well-established principle of international human rights and is possibly the most difficult one to define because of the reasons mentioned above. Nonetheless, it can be divided into four related aspects (Electronic Privacy Information Centre Washington, DC, USA, 2004): • Bodily Privacy- refers to the protection against any unwanted intrusion on a person’s physical self. The right to bodily privacy has been considered a critical part of the right to privacy; • Information Privacy- with the increasing digital presence of individuals giving rise to the creation of personal data, the ideals of information privacy have been incorporated within the core principles of the right to privacy; • Privacy of Communication- the rights to freedom of speech, expression, movement, and association are the core principles of international human rights that are essential for an individual to realize the right to privacy of communication. The right further spreads over electronic forms of communication such as telephone, emails, and other forms; • Territorial Privacy- refers to the protection against any unwanted intrusion into the domestic territory of an individual; The Right to Privacy under International Law The right to privacy is an established and universally accepted human right. The same has been stated as an intrinsic and inviolable human right under the Universal Declaration of Human Rights and the International Covenant on

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Civil and Political Rights. Certain regional instruments have also recognized the right to privacy, such as the Council of Europe6. The shift on digital platforms and their increasing penetration in the society has opened the floodgates for a number of concerns including privacy violation issues. This has given the way for a relatively new concept of the right to obscurity in cyberspace. The traditional notion that human interactions are limited to public spaces is changed and private human interactions might also take place in public cyberspace. Considering the difficulty in identifying and defining an individual’s privacy, it is argued that the right to obscurity is a practical and achievable goal (Obscurity by Design, 2013). Obscurity is the state of being unidentifiable online (The Case for Online Obscurity, 2013). A user is considered as being obscure when a casual observer does not possess sufficient information to decipher the fragments of that individual’s personal data (Privacy as an International Human Right and the Right to Obscurity in Cyberspace). Artificial Intelligence and its subsets majorly train and function on datasets that consist of individual data, often personal data. Therefore, it is essential for the right to obscurity to be involved within the aspects of right to privacy. Privacy and National Security Dynamics Perhaps the most discussed subject is the balance between an individual’s right to privacy and the restrictions on it due to national security considerations. The 6

“Article 12 of the Universal Declaration of Human Rights- No one shall be subjected to arbitrary interference with his privacy, family, home or correspondence, nor to attacks upon his honour and reputation. Everyone has the right to protection of the law against such interference or attacks.”

“Article 17 of the International Covenant on Civil and Political Rights1. No one shall be subjected to arbitrary or unlawful interference with his privacy, family, home or correspondence, nor to unlawful attacks on his honour and reputation. 2. Everyone has the right to the protection of the law against such interference or attacks.” “Article 8 of the Convention for the Protection of Human Rights and Fundamental Freedoms, as amended by Protocols No. 11 and No. 141. Everyone has the right to respect for his private and family life, his home and his correspondence. 2. There shall be no interference by a public authority with the exercise of this right except such as is in accordance with the law and is necessary in a democratic society in the interests of national security, public safety or the economic well-being of the country, for the prevention of disorder or crime, for the protection of health or morals, or for the protection of the rights and freedoms of others.”

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role of surveillance is important in the regular governance of a country. Therefore, it is necessary to strike a balance between an individual’s right to privacy and government sponsored surveillance. Surveillance can be beneficial to gather information and exercise appropriate control over the subjects of a country. However, unchecked surveillance can cause serious damage to the democratic institution of a country as well as infringe the basic rights of its citizens. As the platforms wherein individuals interact diversified, the concept of surveillance also transformed to match it. The changing economic and social structures in the modern society have caused a significant need for growth in surveillance. It has to be understood that surveillance is not the initiation of social change but is a response system to the increasing dependence on information for social control in a modern society (Striking the Balance Between Privacy and Governance in the Age of Technology, 2016). Artificial Intelligence, Machine Learning and Big Data Analytics have transformed the way data was studied (Privacy vs National Security, 2020). Large amount of data, whether personal or not, is analysed to generate meaningful and actable outcomes and predictions. This data is sometimes masked to protect the privacy of the data principal but most of the times this essential practice is not followed unless mandated by domestic regulations. Therefore, it is necessary to safeguard this right even more when such advanced technologies are at play that have the capabilities to infringe the basic human rights of individuals in the cyberspace. The other side of this argument is raised by commentators that worry about national security. It is well established that the price of national security is individual privacy. National security cannot be compromised in these modern times where terrorism and global crimes, including cybercrimes, have become so advance. Therefore, it is important to draw a line and strike a balance between the two. The recent revelations by a former NSA Contractor have initiated serious debate about the balance between individual privacy and the need for national security. The same has been discussed in the case study section of the chapter. Concept of Legal Regulation and Regularization (Electronic Frontier Foundation) This section specifically deals with the international accords and other instruments that deal with privacy and serve as a guiding framework for international instruments, regional treaties, national laws, and other policy frameworks.

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• Organization for Economic Cooperation and Development- OECD is a forum working towards the protection of privacy and transborder data flows. It has 30 countries as members that develop guidelines on the subject and the commitments from the decisions are undertaken on a voluntary basis. The OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data, 1980 is a recommendation document and guidelines on the subject matter. • The Council of Europe- COE is a forum of 47 member states that work towards citizens’ privacy rights. The forum advances general as well as specific agreements to safeguard rights as a part of a broader agreement and commitment for the development and protection of human rights and certain other fundamental freedoms. The Convention for the Protection of Individuals with regard to Automatic Processing of Personal Data, 1980, and the Additional Protocol to the Convention for the Protection of Individuals with regard to Automatic Processing of Personal Data regarding supervisory authorities and transborder data flows, 2001, are the two of the most comprehensive documents by the Council of Europe on the subject matter. • Asia Pacific Economic Cooperation- The APEC has 21 member states and is an inter-governmental organization. It makes decisions on the basis of consensus and the commitments arising from it are undertaken on a voluntary basis. • The European Data Protection Directive- The Directive was adopted by the European Union that have been subsequently transposed into national laws by 27 of the member states. The Directive is designed to protect the collection, use, and disclosure of personal data within the ambit of public and private sectors. • International Conference of Data Protection and Privacy CommissionersThe Conference brings the highest authorities and respective institutions that guarantee data protection and privacy. The Conference is an annual event with experts in the field of data protection, privacy and other related fields from every continent. • The Guidelines for the Regulation of Computerized Personal Data Files, 1990 (E/CN.4/1990/72) • The Convention for the Protection of Human Rights and Fundamental Freedoms, 1950- Article 8 of the Convention deals with the right to privacy. • The Universal Declaration of Human Rights, 1948- Article 12 of the Universal Declaration of Human Rights specifically deals with the inherent and inviolable right to privacy as a basic and core principle of international human rights. It reads as, “No one shall be subjected to arbitrary interference with his privacy, family, home or correspondence, nor to attacks upon his

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honour and reputation. Everyone has the right to protection of the law against such interference or attacks.” • The International Covenant on Civil and Political Rights, 1966- Article 17 of the International Covenant on Civil and Political Rights deals with the right to privacy. It reads as, (1) “No one shall be subjected to arbitrary or unlawful interference with his privacy, family, home or correspondence, nor to unlawful attacks on his honour and reputation. (2) Everyone has the right to the protection of the law against such interference or attacks.” • The Convention on the Rights of the Child, 1989- The Convention deals with the right of privacy of a child. It reads as follows, “(1) No child shall be subjected to arbitrary or unlawful interference with his or her privacy, family, home or correspondence, nor to unlawful attacks on his or her honour and reputation. (2) The child has the right to the protection of the law against such interference or attacks.” In addition to the afore-mentioned international instruments regarding the right to privacy and individual data protection, there are many national laws and policies that countries have adopted to protect the right to privacy and ensure data protection of their citizens. Some of these laws and policies are: • The European Union General Data Protection Regulation, (EU) 2016/679 made by and passed under the Council of European Union and the European Parliament. • The UK Data Protection Act, 2018. • The Personal Data Protection Bill, 2019. As of March 2020, the Bill is being analyzed before the Joint Parliamentary Committee with the involvement of subject-matter experts and other stakeholders. • The California Consumer Privacy Act of 2018 and the California Privacy Rights Act.

Conjunction with Artificial Intelligence Artificial Intelligence and Privacy Basics The rapid evolution of Artificial Intelligence and its related technologies and their ability to analyse and process personal information of data principals raises important concerns their right to privacy. These technologies, due to their increased potential in predicting practical and actable outcomes, have spread over a vast array of fields. The advantages of these technologies which enable them to process information and produce desired results in a more efficient manner, while performing tasks that would not be humanly possible, makes them so desirable. Nevertheless, uncertainty regarding the collected data for

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their sufficient protection from the perspective of privacy still prevails. A number of countries do not have sufficient data protection laws and regulations that ensure data protection. Therefore, majority of the countries do not mandate compulsory consent before collection of general, personal or critical data of their citizens. Consenting to give personal or any other information about oneself makes it amenable to be monitored by the concerned personnel for their own purposes. For instance, the use of facial recognition systems has the potential to pose the threat of invasion of privacy. Especially when countries like China have been deploying these mechanisms as part of their authoritarian schemes (Mozur, 2019). Apart from the government, even private players use such technologies for their commercial benefit. Predictive abilities of Artificial Intelligence and its subset technologies enable them to analyse the personal data of their consumers and other individuals to generate targeted recommendations. Most AI technology systems are self-sensing/ self-learning technologies which study consumer patterns and make a digital profile. This profile may contain data about the individual which may range from basic and unidentifiable information to sensitive and critical data. Data-Algorithms Relationships and Privacy In the modern society the opportunity for state sponsored surveillance is almost limitless due to the constant internet connectivity, cameras with audio and video recording capabilities, as well as other surveillance tools (Purpose and Function Creep by Design: Transforming the Face of Surveillance through the Internet of Things, 2013). Humans have the capacity to process only a limited amount of data whereas AI enabled computer systems have the capabilities to process almost infinite amount of data. Data mining and surveillance for effective law enforcement and national security do raise serious human rights concerns (Comparing Surveillance Powers: UK, US and France, 2015). Apart from the concerns of surveillance, certain other concerns such as unwarranted disclosures, discrimination, abuse of individuals due to disclosure. To counter all these issues, various privacy preservation methods can be opted by data processors, whether public or private. These methods are often used by algorithmic developers to safeguard individual privacy. Three of the most relevant data anonymization techniques are (Samal, 2020): • k-anonymity: an anonymization method where specific quasi-identifier columns in training datasets are removed or changed, so that at least two of the remaining columns share the same or similar attributes. This method

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ensures that at least certain ambiguity of a minimum ‘k’ records for a targeted query search that might contain any individual privacy-sensitive attributes. • l-diversity: this technique is generally implemented in addition to the previously mentioned method, k-anonymity. It substitutes for the weak points of ‘k-anonymity’. • t-closeness: it can be defined as, “the distinction between the distribution of a sensitive attribute in an equivalence group and the distribution of that attribute on the whole table (or population) is no more than a threshold ‘t’.” (Security and Privacy Considerations in Artificial Intelligence and Machine Learning , 2019) Artificial Intelligence as a Legal Entity A crucial question that arises when delving into the question of privacy and holding the system accountable, is that of assigning legal personhood to AI systems. The extent of anonymity that the system possesses which makes it possible to hold them as distinct entities under the law such as corporations, companies, religious idols and others. Contemporary legal systems offer a limited insight into the same as they are grappling with the issues surrounding assignment of such rights and imposing liabilities on AI systems. It is the metaphysical nature of the AI technology which calls for an entitycentric point of view (Placing Blame, 1997). For an entity to be held as a legally distinct entity and personality of its own, it has to be autonomous and rational. Those who advocate in favor of assigning legal personhood to AI state that giving them this status would enable the present legal systems to be able to gradually incorporate the AI, instead of having to make major reformations. For instance, the computer program called Deep Blue, a computer program that plays chess developed by scientists at IBM defeated the chess legend, Gary Kasparov, in a match (History.com). This shows the kid of prowess the AI technologies have to acquire potential much beyond the very humans who developed it. In the instances where the AI technology have been known to function autonomously, require very little human involvement while performing tasks, the question of liability becomes an important one. If the AI is simply considered as an agent instead of being a legal entity of its own, the human who developed the technology would be inadvertently held accountable for a wrongdoing, even though the AI system was not intended to perform in such a manner. The rapid technological developments in this field can be expected to produce robust AI systems which would function closely like humans and the kind of

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independence would call for a grant of a separate recognition under the law. This would also promote more activity in this field by eliminating the threat of the developers being always held liable vicariously. Artificial Intelligence and Civil Liability When the question of imposing liability on AI systems, both criminal and civil liabilities can be imposed upon them. The issue that requires deliberation are in which situation and who would be held liable for damages. It is understandable that the human-developer would be held accountable for product liability should the AI malfunction. But would it be possible to hold the AI system itself for damages? The European Parliament in the 2017 European Parliament Resolution on Civil Law Rules on Robotics stated that: “creating a specific legal status for robots in the long run, so that at least the most sophisticated autonomous robots could be established as having the status of electronic persons responsible for making good any damage they may cause, and possibly applying electronic personality to cases where robots make autonomous decisions or otherwise interact with third parties independently”. The notion of “Electronic Personhood” can be interpreted in two forms (European Parliament, 2020): • Electronic personhood as the acknowledgment of individual rights of the artificial agent: radical inadmissibility. • Electronic personhood as the equivalent of legal personhood. The base for establishing a liability on AI systems can be successful only when a sector-by-sector approach is taken. The basis and nature of liability would then depend upon the type and the intensity of the risk that is associated with the use of AI (Civil Liability for Artificial Intelligence: What Should its Basis Be?, 2019). Machine Learning and Privacy: Ethics and Implementation This section is edited in a point-wise basis for better understanding and precise content presentation. • Machine Learning is employed for multiple purposes ranging from “intrusion detection” to using them to give suggestions, recommendations, as well as future predictions.

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• The data for these systems are stored at centralized locations in a form of clear text, which is accessed by algorithms which is then studied and comprehended through machine learning systems upon the basis of which a suggestive framework is developed. • A common threat faced by the data principals whose data, whether personal or not, is at stake, is that the large scale of data which is collected may be used for multiple purposes and might be misused in ways that are unknown to them.

Case Studies Snowden’s Revelations The National Security Agency (NSA) of the United States of America deployed a domestic surveillance program known as the “President’s Surveillance Program” to monitor and carry out other surveillance related activities on the people inside the United States that might have connections and associations with terrorism. This giant step was taken as the aftermath of the attacks on the World Trade Centre on September 11, 2001. The United States Government persuaded all the telecom companies to furnish all call-detail records of all customers. Thereafter, the NSA fit communication surveillance equipment in these telecom companies that would allow the NSA to intercept all communications. These communication mediums include phone calls and text messages among other mediums. The network of interception was built around a data-mining program known as PRISM. This program had the back-door access to the servers of companies such as Yahoo, Microsoft, Google, Apple, Facebook and Skype. These companies control the world’s most powerful and popular social media, video, and email platforms. Although the motive of this operation was to counter and intercept all terrorism and related crimes, perhaps the NSA took the legal maxim, ‘the end justifies the means’, in its literal sense. A former NSA Contractor, Edward J. Snowden, leaked and revealed the aforementioned facts after which the citizens of the US and other affected individuals portrayed their dissatisfaction (Harding, 2014). These operations clearly against the principles of the right to privacy and in violation of a dozen US privacy protection laws as well as international instruments that guarantee the protection against such unwarranted government programs. These revelations caused the debate regarding individual’s right to privacy and national security considerations to increase exponentially.

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It is also pertinent to note that the primary role of a state is the welfare and security of its citizens, and without prior notice of information and intelligence countering terrorism is next to impossible. Therefore, to achieve these objectives, security agencies and the Government need to equip themselves with the necessary tools and technologies. However, unrestricted and unregulated interference with citizens’ privacy will result in severe violations of their basic and fundamental human rights.

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International Intellectual Property Law

Abhivardhan and Dev Tejnani

Introduction and Conceptual Basis For the purposes of this chapter, we are going to use the definition of intellectual property accepted by the World Intellectual Property Organization as per Article 2(viii) of The Convention Establishing the World Intellectual Property Organization (WIPO) (hereinafter referred to as ‘WIPO Convention’)7. Now, the understanding of intellectual property stems from the rise of structural globalization and multilateralism. Since the formation of the United Nations, the internationalization of the world rendered the case to focus on how to preserve creativity and in general, intangible works produced and owned. It is clearly obvious that the digitization of the international community enabled the information age, where internet and cyberspace also became an important coordinate to regulate intellectual property rights among various actors. There is no doubt in acknowledging the fact that intellectual property law, when even it did not exist like it does now, in the Victorian times, in England, in 14th and 15th centuries, was very much limited to the idea of monopoly over anything that was created by some entity per se (Rethinking the Development of Patents: An Intellectual History, 1550–1800, 2001 p. 1255). However, proponents of libertarian ethics have generally criticized the concept of intellectual property, which is important to be noted, which will definitely be dealt in this section.

• • • • • • •

“[i]ntellectual property shall include rights relating to: literary, artistic and scientific works, performances of performing artists, phonograms and broadcasts, inventions in all fields of human endeavor, scientific discoveries, industrial designs, trademarks, service marks and commercial names and designations, protection against unfair competition,

and all other rights resulting from intellectual activity in the industrial, scientific, literary or artistic fields.”

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Libertarianism and the Praxeological Relevance of IP

The concept of IPR was mostly forwarded by scholars in the field of law, who believed in utilitarianism. The concept was simple: support innovation with limited caveats that such rights are not used to exploit monopoly over content, to balance with the policies of social welfare. Now, libertarians believe that IP historically has been more connected with the praxeological purpose of monopoly and institutionalizing control over the entrepreneurial work and its trends developed within or outside the jurisdiction of the state. It can also be argued that IP is central to state and corporate interest as the juridical systems of law support or endorse it, and that generally drives IP regulation. Economists like Ludwig von Mises have also argued interestingly, that ideas which form some basis of an entrepreneurial trend of work, which generally we call, practically, as fulfilling dream to make a startup or democratize something you have ideated. Now, if you are creating something through scarce means (here ideas are generally viewed as the preconditions of agency) (On the Impossibility of Intellectual Property, 2020 pp. 33-45), to get your specific goals achieved, that achievement sought by you, should not be generally replicative, because that is even being a biproduct something unique (The Entrepreneur: Real and Imagined, 2008). It does not mean that we should merely accept the common anti-IP argument that an idea is a generally prerequisite of production and not subject to ownership. However, owing to artificial intelligence’s dynamic practical behaviour, it is important to take a note of the libertarian vision of criticism, as it allows grounds for people to deal and estimate how IP law may require practical reforms to some extent. Interventionism through IP laws is again, based on state consequentialism, which is important to note.

Legal Background In line with international intellectual property law, the classification of protection of the list of sorts in Article 2 (viii) of the WIPO Convention is provided as follows: • • •

Literary, artistic and scientific works are protected under the category of copyright branch of IP; Performances of performing artists, phonograms and broadcasts are protected as related rights to the copyright branch of IP; Inventions, industrial designs, trademarks, service marks and commercial names and designations belong to the industrial property branch of IP

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which is also related to Article 1(2) of the Paris Convention for the Protection of Intellectual Property8; Scientific discoveries, however, are not the same as inventions in IP Law9;

Fields of IP Protection This sub-section specifically discusses the fields of IP Protection recognized under WIPO Law. Patents. Patents generally authorized a legal exploitation (manufacturing, selling, using or importing) of some invention, at the authority of the owner of the invention or the inventor, so to mention, which are granted by governments. Generally, patents do not provide any explicit right to make use, sell or import the invention, but it anyways protects the owner of the patent in a negative legal sense from any unauthorized usage accrued by any third party. Patent rights are enforceable – in general by the patent owners, unless if there are some specific emergency or extra-adversarial cases where the government seeks due intervention. There are many conditions of patentability, which are required to be achieved in order to gain patenting. It depends upon the kind of subject matter that has to be reckoned as an invention to be patented. Now, in common understanding, the content should be industrially useful, novel, must have an inventive step (non-obvious) and the disclosure of the patent application must surely meet some standards. The general rule is that patent protection is available for the inventions in all the fields of technology. A patentable subject matter, therefore, in lines with the TRIPS Agreement, Article 27.1, is usually established by the statute and is defined by the exceptions of patentability10. Also, the industrial applicability of

The industrial property branch of IP also covers trademarks, service marks, commercial names and designations, including indications of source and appellations of origin, and protection against unfair competition. 9 The Geneva Treaty on the International Recording of Scientific Discoveries (1978) defines a scientific discovery as “the recognition of phenomena, properties or laws of the material universe not hitherto recognized and capable of verification” (Article 1(1)(i)). 10 Article 27.3 of the TRIPS Agreement: Examples of fields of technology which may be excluded from the scope of patentable subject matter includes the following: 8

• discoveries of materials or substances already existing in nature; • scientific theories or mathematical methods; • plants and animals other than microorganisms, and essentially biological processes for the production of plants and animals, other than non-biological and microbiological processes;

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an invention has to be understood in a broader sense, where application stands on a large scale. Then, novelty becomes an undisputed aspect of an invention to make it eligible for patenting. Novelty however is not something which can be proved or established. Absence of novelty however can be established or proved. Therefore, an invention if is not anticipated by ‘prior art’, is ascertained to be a new invention. On the question of non-obviousness or the inventive step, a basic question is asked whether or not the intervention “would have been obvious to a person having ordinary skill in the art”, which practically, at a substantial level, is indeed very hard to determine. A proper disclosure should additionally be given in the patent application to the concerned government herein to ensure and estimate the propriety of the application itself. Copyright & Related Rights. Copyright law is a specific branch of IP Law, which deals with the rights of intellectual creators. At most, copyright law is concerned with the particular forms of creativity, especially the realm of mass communication. Copyright law however, only protects the form of expression of ideas, but not the ideas themselves, so as to prevent the forms to be ‘copied’ and appropriated by someone else. At national levels, the enrichment of national cultural heritage is often related with the copyright protections/IP protections which are preserved strongly and reasonably. Now, the subject-matter of copyright protection stems up to literary, artistic and scientific works, whatever be the mode of expression. However, the subject-matter must be an original creation. Also, protection granted here is independent of the value and quality accorded to the work. Thus, to be protected via copyright law, the work has to originate from the author. Now, the owners of a copyright can use their work as they wish to, but not with none of the regards to the legally recognized rights of others. They can also authorize to exclude others from using their work by mandating permissions to them if they wish to reproduce or republish it. The Berne Convention requires member countries to grant to authors: • the right to claim authorship of the work; • the right to object to any distortion, mutilation or other modification of, or other derogatory action in relation to, the work which would be prejudicial to the author’s honor or reputation;

• schemes, rules or methods, such as those for doing business, performing purely mental acts or playing games; • methods of treatment for humans or animals, or diagnostic methods practiced on humans or animals (but not products for use in such methods).

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The moral rights stay with the author, even if the economic rights of the author have been taken away. With regards to ‘related rights’, which are also called as the neighbouring rights, are of three kinds: • the rights of performing artists in their performances; • the rights of producers of phonograms in their phonograms, and; • the rights of broadcasting organizations in their radio and television programs. The reason why we have this conception is because copyright is not just about rights against infringement/infringement prohibition or preventing the use of the content merely. In the process of expression of the work, the contributors, who make the communication and so, expression of the work possible, require their rights to be protected. On the question of ownership, copyright ownership is clearly exclusive, and is granted to the original author(s) unless the national law mandates any subject exception to the same. Now there exists limits on copyright protection too. In the case of a temporary protection, state parties to the Berne Convention can protect the copyright of the author until 50 years after his death. Nowadays, the trends have changed, the 50-year extension is being elongated. Trademarks. Trademarks are much ancient in terms of their existence. Their economic importance has grown much faster in the years of globalization since the end of the Cold War, where now they have become a key factor in international trade and market-oriented economies11. Now in order to individualize a product, the source of the product must be identified. It however does not mean to inform the consumer the exact person who has manufactured the product or is trading it, but it means that the trademark should indicate the distinctiveness of the brand of the product, and so the industry in which the product is categorized in. Even a simple reliance on the distinguishing function of the trademark is enough. There are two basic requirements to get a trademark, internationally. The first one is the basic function of the trademark, which is distinguishability. The second important requirement is the possible harmful effects of a trademark, if it affects public order or morality. Similar provisions exist in the Articles Article 6 quinquies A & 6 quinquies B of the Paris Convention. These 2 form the criteria of protectability. The best definition of a trademark according to the World Intellectual Property Organization is as follows: “[A] trademark is any sign that individualizes the goods of a given enterprise and distinguishes them from the goods of its competitors.” 11

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Industrial Designs and Integrated Circuits. Industrial Design refers to the right granted pursuant to a registration system, to “protect the original ornamental and non-functional features of an industrial article or product that result from design activity”. Generally, visual appeal drives as one of the considerations to drive consumers to make decisions to consume the products made by the industry, thereby if the industrial designs are protected, the success of markets is also preserved, as the distinctiveness of the designs are protected through the same. These are the following characteristics of industrial designs: • • • • •

the definition of the subject matter of protection; the rights which apply to the proprietor of the subject matter; the duration of such rights; the entitlement to such rights; the method of acquisition of such rights;

Definition of Subject Matter of Protection. The subject matter of protection in the case of industrial designs is not some articles or products, but the design employed. The protections granted to industrial designs here do not render any exclusive rights to the owner to exploit the protection and ownership established. Instead, the protections exist where any industrial design embodies the one which is protected under IP Law 12. What distinguishes between industrial design protection and copyright protection is the fact that the former protects design on with a utilitarian legal purpose, while for the former, the purpose is to protect the aesthetic creations. Rights Applicable to the Proprietor of the Subject Matter. Generally, the rights of legal protection of the industrial designs are vested within the author/originator of the industrial design(s). On the question of entitlement of such rights, the employer and not the contractor preserves the rights of legal protection. If there is any intermediary like a computer being used to develop an industrial design, then it would be legible that the person who manipulates the computer’s design in such a manner, can develop the design and is so the designer here. Acquisition of Rights. An application generally would be assessed first pursuant to which after registration, the industrial designs would be granted to the person. Unless there is any opposition out of interest, the application would be assessed on the basis of the standards they have. 12

For example, the definition of “design” which is used in the Registered Designs Act 1949 of the United Kingdom, for instance, refers to “features of shape, configuration, pattern or ornament” (Section 1).

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Integrated circuits in IP Law is attributable to the protection of the topography of the integrated circuits, which are protected. The layout designs of the integrated circuits are the creation of human mind, which requires much time, is expensive and requires relevant experts. The smaller an integrated circuit, the less the material needed for its manufacture, and the smaller the space needed to accommodate it. Geographical Indications. There are certain words, which represent geographical connotations, which require utmost protection. Places like ‘Tequila’, ‘Champagne’, ‘Havana’, etc., represent geographical indications. Now, there is no such mention of geographical indications in the Paris Convention for the Protection of Intellectual Property, but the phrase ‘indications of source’ in the Article 1(2) of the Convention is interpreted to include geographical indications altogether through explicating the difference between indications of source and appellations of origin. WIPO has chosen this term to recognize forth the appellations of origin. This term has been used in EC Council resolutions and even the TRIPS Agreement. Arbitration and Mediation of Intellectual Property Disputes In matters related to intellectual property disputes, the two ways which are generally adopted are arbitration and mediation. Methods like Expert Determination are also used in IP disputes. When it comes to enforcement, the important thing of understanding is that most of the technology agreements are international, which renders some difficulty in handling dispute resolution altogether. WIPO has done some impressive work by establishing centers of arbitration and mediation since September 1993. The Center provides the following kinds of dispute resolution mechanisms: • Arbitration • Mediation • Expedited Arbitration In the third case, the rules limit the procedural steps in the arbitration to ensure quicker solutions are reached. Now, post any of the three mechanisms, WIPO offers any of the 2 services to the parties in dispute: either to add model contract clauses to submit future disputes to such procedures, or using the submission agreements to submit any future disputes to one of the rules, the WIPO Arbitration Rules, the WIPO Expedited Arbitration Rules or the WIPO Mediation Rules. WIPO also provides dispute resolution mechanisms in its Uniform Domain Name Dispute Resolution Policy since December 1999.

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Conjunction with Artificial Intelligence Artificial Intelligence has reached its zenith and it can be deemed to be regarded as something which is omnipresent. Artificial Intelligence and the systems of Machine Learning was first founded by Alan Turing, who aimed to deem machines as a form of intelligent device, which had the capacity to undertake difficult tasks which humans could not perform, or found difficult to perform. Alan Turing was of the opinion that machines can work in consonance to humans and in fact aid humans in the way they carried out their various tasks (Turing, 1950 pp. 433-460). Ever since Alan Turing developed his infamous coding machine, AI has reached its zenith and opened up a number of avenues, thanks to the introduction of Big Data and the improvements that have been brought about when it comes to computing powers of a particular data set. It is imperative to understand that IP rights and trade secrets of an organization have the capacity to drive a wedge between the various norms of IP laws and it can also be said that there arises a need to understand the various aspects revolving around transparency and accountability of these innovations. Regardless, there arises an aspect with regards to the disclosure which can be deemed to be regarded as those aspects which do not take into its ambit algorithmic rules, however they only stick upon the results, pertaining to the explanation of the laws which can be deemed to be regarded as a result of its application. Basically, there is no inherent risk when it comes to breaching or violating the provisions of IP laws and trade secrets. Moreover, such rights cannot be regarded as something which have the capabilities to disregard the aspects pertaining to disclosure and the explanatory nature of AI systems. This pertinent issue can be deemed to be regarded as an issue which delves into the inherent overlap between AI and IP laws. The innumerable questions which have been raised above, come down simply to state that the overlap between the innovations which are a result of AI and the protections guaranteed under IP law are extremely intriguing and there can be a number of observations that can be made when dealing with these two aspects, however, there needs to be a balance between AI and IP. Artificial Intelligence and its Growth

It is imperative to note that the term, “artificial intelligence” was first coined or was majorly popularised by John McCarthy and Marvin Lee Minsky, who could be deemed to be regarded as the organizers of the 1956 conference on AI, which was held in Dartmouth which specifically enumerated upon the usage of AI and how AI can be deemed to be regarded as a separate branch of research in itself. AI can be deemed to be regarded as a set of systems which

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have the capacity to understand and analyse intelligent behaviour or mannerisms in consonance to analysing the environment in which they operate and at the same time has the capacity and the autonomy to take action, and to achieve a series of goals for which it has been designed (Joint Research Centre, Europe, 2018). Intelligence can be deemed to be regarded as an important aspect when it comes to understanding the functions which are carried out by a machine, and this machine has the capacity or rather the capabilities to analyse and understand cognitive functions which may be primarily associated with the actions taken by humans or by an animal brain, which can also be deemed to be regarded as the ability to learn and analyse problems. Machine Intelligence can further be reiterated by stating that it may take into consideration active thinking or acting in accordance to the behaviour of a human being, which can also be deemed to be regarded as a cognitive approach or a rational, computational approach (Artificial Intelligence: A Modern Approach, 2016). However, for a number of decades, AI can be deemed to have experienced a number of ups and downs, which can also be called as the, “winters” of AI, however, at present AI can be deemed to be regarded as a part of every individual’s daily life, whether it comes to making use of a personal virtual assistant or whether it comes to using a mobile phone or whether it comes to driving or travelling a semi-autonomous vehicle. It can also be deemed to be regarded as a method which enables individuals to solve problems in a jiffy or understanding and scrutinizing complex problems when it comes to the field of medicine, public policy or enabling lawyers and judges to find complex solutions when it comes to handling justice. The way AI adjusts, makes it extremely easy for individuals to make its use in a number of sectors and therefore a number of objectives can be undertaken with the use of AI, for instance, AI can aid individuals in carrying out automatic natural language processing, the representation of knowledge, carrying out automated reasoning, making use of the various techniques pertaining to machine learning, analysing neural networks, understanding computer vision, the development of robotics, et cetera. In this last aspect, AI system can be deemed to be regarded as a system which is not only limited to its software; however, it also works towards the integration of AI systems in hardware. Artificial Intelligence and Intellectual Property Laws With regards to all the aspects mentioned above, it can be said that with the introduction of new technology, there arise a host of issues pertaining to the intellectual property rights. As a matter of fact, there exist three basic overlaps when it comes to AI and IP Laws. The first being, AI as a technology which can be used in order to assist or guide an individual when it comes to the management of intellectual property rights; the second aspect that falls under

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the ambit of AI and IP laws is how can intellectual property deemed to be regarded as a secure regime when it comes to the protection of AI and thirdly, how can IP be reiterated or can be deemed to be regarded as an obstacle when it comes to the transparency pertaining to AI systems. The relationship between all of these three correlating overlaps exists when IP has the capability to take under its ambit the various influences that fall in line with the usage of AI and how AI has the capacity to influence IP or IP Laws. Moreover, this overlap between the various aspects of IP rights and laws and AI can be deemed to be regarded as a moot point, which has two sides to it- it can be beneficial and at the same time it can also be deemed to be regarded as something which has a conflicting effect. First and foremost, it is extremely imperative to understand that AI is something which aids humans and AI has been proven to help and aid individuals in understanding their intellectual property rights. For instance, the use of AI in the administrative levels of Intellectual Property Rights can be made much simpler and easier with the use of machine learning techniques which could be deployed when it comes to the administration of the innumerable applications which fall under the garb of IP protection. One such example is the World Intellectual Property Organisation’s Translate and its Brand Image Search software. This software’s are AI-based software’s which have the capacity to carry out autonomous tasks such as translating words and paragraphs or recognising images or patterns. In fact, there are innumerable IP offices around the globe which have turned to AI in order to carry out their tasks and they are of the opinion that the deployment of AI tools has made the process of granting IP protection much easier and much simpler. In fact, in the year 2018, a meeting was set up by the World Intellectual Property Organization (WIPO) in order to analyse and deliberate upon the usage of AI applications and ML techniques, which enabled the sharing and the exchange of a lot of crucial information (Conversation on Intellectual Property (IP) and Artificial Intelligence (AI): Draft Issues Paper on Intellectual Property Policy and Artificial Intelligence, 2019). The second aspect which needs to be covered here is with regards to how intellectual property can be deemed to be regarded as a part of the legal system which has the capacity to protect AI. AI tools and various ML techniques are already being deployed in the justice system in a number of jurisdictions, however, the introduction of these autonomous tools in the justice system is ought to create a number or a series of significant impacts upon the creation, production and distribution of cultural goods and services. In fact, it can safely be said that the policies that are envisioned within the scope and the ambit of IP laws are aimed towards fostering innovation and creativity in the economic and technological sphere- this is where AI and IP laws merge and intersect with each other. There are innumerable rights that fall under the scope of IP regime

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and which are specifically aimed towards protecting artificially intelligent innovations. At this conjecture, the World Intellectual Property Organization, i.e. WIPO is bolstering its resources along with the encouragement that it has received vide its member states, which particularly focuses on collating the various aspects that fall under the ambit of the government instruments which are relevant to the standards laid down by Artificially Intelligent systems. Patent and Copyright codes can be deemed to be regarded as two of the most relevant or apt systems when it comes to the protection of AI and its related technologies, especially when there are inventions which are made via the use of AI tools. There are a number of reported cases when it comes to dealing with IP laws in the field of AI. For instance, patent protection cases wherein the applicants have gone ahead and named their invention after their inventor, in such cases, the first and the foremost question that arises is whether the provisions of international law grant a sanction to innovators to name their AI innovations after their inventor and if so, there need to be provisions embedded within the various international legislatures which specifically enumerate upon how a human inventor can name his/her AI innovation after himself/herself or after its creator, or whether the necessary stakeholders who are affected by the innovation should have autonomy or should have a discretion when it comes to granting or filing for a name with the requisite IP office. Yet another serious question that arises here is whether the individual who files for a patent application, attempting to patent his AI innovation be deemed to be regarded as an owner of the patent or not, and whether there are enough provisions within the various international legislatures which have the capacity to grant IP protection to innovation which are autonomous or created by way of an AI application. Furthermore, it can be safely said that when it comes to the interpretation of patents, the aspects with regards to an inventive setup or non-obviousness are ought to arise. Lastly, the various conditions pertaining to the clauses of disclosure can be deemed to be regarded as an extremely strenuous task when dealing with inventions which are a result of an AI application. The primary concerns that arise here are how can disclosures be fulfilled when the algorithms which are powered into the AI devices constantly change over time, in fact, it can be an extremely arduous task when it comes to an AI software that is powered or that runs on “black-box” algorithm, which is something that cannot be identified easily, which leads to the question as to whether an AI application which runs on black-box algorithm can be granted IP protection or not. It is imperative to understand that these are certain conflicting aspects which need to be given special emphasis and the lawmakers of various countries should come up with a sui generis system which specifically reiterates upon IP rights specially for AIpowered inventions and these inventions can be deemed to be regarded as inventions which have the capacity to adjust according to the various

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innovations that take place in the field of AI. Artificial Intelligence and Copyright Laws A host of similar questions are ought to arise when it comes to dealing with the copyright regime. AI applications can be deemed to be regarded as applications which have the capacity to generate literary and artistic works, in an autonomous manner and these applications do not fall in line with the provisions of the copyright system which are innately related to the human mind, and with regards to the respect and the reward which can be deemed to be regarded as a matter of expression which falls under the scope of human creativity. One of the most inherent question that arises at this conjecture is whether the protections guaranteed under the copyright regime can be deemed to be regarded as original literary works or whether they can be deemed to be regarded as artistic works which are a result of autonomous AI tools and whether this requires any sort of human intervention. Furthermore, if an individual regards AI system to be protected under the garb of IP rights then under such circumstances, is it viable for IP rights to provide the necessary safeguards when it deems to act as a barrier to AI systems. In fact, it is imperative to understand that in the recent times, the aspects of transparency and accountability of algorithmic decision-making systems has gained momentum, however there still is an underlying question and that is whether the processes involving machine learning have the capacity to satisfy multiple sources, dynamic developments and deal with the elements that are opaque and lastly whether they are used for technological or legal reasons (R.Wexler, 2018 pp. 1343, 1373-74).

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International Cybersecurity Law

Dev Tejnani

Introduction It is imperative to understand that sovereignty and autonomy are two crucial aspects that need to be pondered upon when cybersecurity comes into picture. International sovereignty and autonomy could be deemed to be regarded at risk, with the ever-increasing issues in the international regime with regards to the development of AI. The development of AI in consonance to the development of cybersecurity is at its nascent stage, however, it is constantly developing and this is where there arises a multitude of issues with regards to privacy and data protection regimes. It is extremely necessary to understand that the combination of AI and cybersecurity raises innumerable ethical and safety concerns.

Legal Background Sovereignty and Autonomy in AI The ever-growing economies of a number of nations have led to the development of a number of priorities amongst nations and sovereignty and autonomy are two such aspects which have gained immense political priority. The governments of a number of countries are of the opinion that their national sovereignty is extremely vulnerable since digital technology and AI have reached its zenith. The development of digital technology in consonance to the development of AI can surely be deemed to be regarded as a step forward, however, with the development of such technologies, there also arise a multitude of security and privacy concerns, which primarily could have the potential to put a damp upon the sovereignty of a nation. The world is witnessing constant cyber-attacks that countries are making upon its opponents and these rising international tensions, thoroughly affect the strategic autonomy and sovereignty of a nation. It may perhaps shake a country’s digital roots, rendering their national secrets vulnerable and out in the open. There is not even a slightest doubt with regards to the sovereignty of a few nations being at stake due to the immense advancements in the field of AI and digitalisation. Kello (Kello, 2017), is of the opinion that “cyber” is a factor which leads to the

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development of a “sovereignty gap”. It is imperative to focus upon both state and non-state actors and how these actors have the potential to exploit or create a wedge between the cybersecurity of a nation. Kello was of the opinion that a mixture of persistent hindrance along with the role played by rogue state actors would lead to the misuse of cyber technologies and cyber-enabled exercises which would be undertaken by a number of non-state actors, from state proxies (Maurer, 2018) to terrorist organizations to other global platforms, which would thereby enable the balance of power in a systematic manner according to the traditional state-based (Westphalian) system which deals with International relations. A lot of politicians and individuals who play a major role in policy making are usually of the opinion that strategic autonomy eventually leads to an end and this end can be deemed to be regarded as sovereignty. Sovereignty and Autonomy are closely connected with data sovereignty, digital sovereignty, technological sovereignty, strategic autonomy in the field of defence and military and financial strategic autonomy. It is imperative to emphasize on strategic autonomy, which could be deemed to be regarded as the ability, with regards to the capacity of a nation to ponder upon the most crucial aspects of a nation’s long-term future and how its economy may fare within its various institutions (Timmers, 2019). However, in the past, strategic autonomy was majorly used in France when it came to dealing with their military and defence services and later it was used by India in order to emphasize on its foreign policy, however, the scope of the term, “strategic autonomy” has now taken a different shape altogether with regards to the concerns surrounding the economy and the society as a whole. A number of states adhere to different approaches while dealing with the scope and the ambit pertaining to strategic autonomy with regards to its development in the digital age. Risk management, which takes under its scope, the various issues pertaining to sovereignty, with specific emphasis on cyber-resilience, strategic partnerships of states that adhere to a similar ideology alongside the inclusion of certain non-state actors having a watch over some of the most prominent technological advancements, with an aim to develop the usage of global common goods and to develop and create certain critical digital assets in consonance to the global interests. A state has the capacity to take over all or some of these approaches in a time bound manner at the same time.

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Conjunction with Artificial Intelligence AI Ethics and Cyber Security with focus on Risk Management When a state decides to adhere to a risk management approach, it takes into consideration the various aspects that fall under the scope of risk management, such as to analyse, protect, understand, defend and recover the potential threats and ensure whether the existing infrastructure is capable of combating these aspects. This approach takes into consideration the proctoring of complex assets, threats based on big data predictions and analysis; at the same time, it also helps to comprehend the various business, legal and ethical issues that surround AI and its growth. It is imperative to understand that AI plays a major role when it comes to dealing with the various business, legal and ethical issues. AI could be deemed to be regarded as a helpful tool which is already marking its niche in the business sector. With the advancements in the field of AI, it has become extremely hassle free to analyse billions of data packets with the help of sensor points, which enables the responsible CERT13 (Computer Emergency Response Team, also called the CSIRT) to concentrate on situations that are extremely crucial and the situations which need to be dealt with in a time bound manner. The New York Stock Exchange is deemed to have been a victim of cyber-attacks and has nearly been attacked half a trillion times a day (RedSeal, 2018). An interesting aspect that needs to be thrown light upon here is the fact that the companies who are indulged in the business of providing cyber based AI resilient solutions are already companies which have a turnover of approximately a billion dollars. There are innumerable ethical issues that come to light whilst analysing the cybersecurity risks associated with the use of AI technology. It is imperative to understand that with the advent of AI, there has been a huge increase in the way businesses monitor their employees. Pervasive methods to prevent risk in consonance to the usage of AI can be deemed to be regarded as extremely coercive and intrusive for a lot of individuals, regardless of whether they are employees at a company or whether they are the citizens of a nation. The extreme monitoring and the extreme usage of AI tools may perhaps make individuals feel that their autonomy and privacy is being taken away, which would again raise a multitude of legal issues giving rise to petitions being filed for the infringement of rights. It is extremely necessary to understand how Deep-learning AI plays a major role when it comes to risk management. Deeplearning AI is a tool which can be deemed to be regarded as an accurate tool, however, an operator using a Deep-learning AI tool may find it extremely difficult when it comes to judging a decision or an opinion made by such a tool. 13

CERT-Computer Emergency Response Team: Computer Security Incident Response Team.

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AI has the capacity to create a sense of freeriding as it may offload the entire responsibility which is been conferred upon it, upon the operator or onto the system via which it is powered. Residual risk is something which can be deemed to be regarded as something which is closely associated with the approach of risk management. If it is looked upon through a financial perspective, then it can be said that such a risk may be offset by a cyber-insurance, however this leads to a political or sovereign question, which needs to be focused upon and that is, how many lost lives could be accepted until the internal legitimacy of the state and thereby the sovereignty of the State is deemed to be regarded as vulnerable or at risk (2017 Wannacry attack that affected many UK Hospitals, led to the loss of innumerable lives (ncbi.nlm.nih.gov). It is extremely necessary to understand the fact that the political perspective with regards to protecting the legitimacy of the state becomes even more fragile when an AI system is put to use and such a system which automatically renders a strategy which is defensive in nature, for instance, the cutting down of an electricity grid which leads to the question as to whether a State really wishes to put the privacy of its people in jeopardy. A lot of experts are of the opinion that the AI systems are extremely complicated and can never be completely protected. The ultimate risk that arises here is with regards to the fact that risk management does not have the perfect potential to detect whether there exists a “kill-switch” option in a system that is being run and whether such an option can be activated at times of an international armed conflict or an accident or an emergency or during the lockdown of an AI infrastructure which is extremely crucial in nature, for instance, tele-communication. Consequently, there also looms a risk with regards to the disclosure of certain intellectual property which may again render the privacy of a country vulnerable or put the national secrets in jeopardy, or perhaps in the wrong hands. It is extremely imperative to understand that the role which AI plays needs to be framed in such a way that it helps to erase the existence of a kill-switch or protects the intellectual property of a country. There are innumerable legal and ethical issues surrounding the use of AI and cybersecurity when it comes to taking the risk management approach. The possible ethical legal issues or threats are: • infringement of the rights of an individual with regards to the protection of his/her autonomy; • with regards to liability, it may not be allocated in a proper manner; • the fallacy of humans in the loop; and • the various legal and ethical issues that arise when a mass surveillance is carried out;

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Furthermore, the legitimacy of a code of control can be challenged inter alia. The algorithms which are programmed in the various software’s used by a country, usually have an intellectual property value and they cannot be inspected properly, since usually these algorithms are developed by other private organizations who have their intellectual property rights in place. In fact, an AI, which is based on a neural network does not have the capacity to make a decision and this could possibly render the data vulnerable. Therefore, when it comes to taking a risk-based approach or a risk management approach, it is extremely necessary to understand that such an approach may render the data vulnerable and perhaps may lead to the occurrence of a host of issues, which may not be in consonance to the ethical or legal principles of a nation, which may not be because of the extensive use of AI, however, the usage of AI will make such a perspective unethical in nature. However, countries can work in consonance to the private sector and can help in the promotion and the growth of norms or the governmental bodies could lay down a set of laws or regulations which could elucidate specifically on provisions that do not erode the privacy or hamper the population and render the data of its population vulnerable whilst making an extensive and fully allocating resources to the use of AI. Furthermore, practical and coherent work needs to be established and put into practice while dealing with the entire AI regime, right from capturing data when it is in the pre-AI stage, to the stage where the AI data derived is processed and further to the stage where the algorithms are explained, i.e. in the post-AI explain ability stage. Countries need to take such steps in order to tactfully combat the increasing number of cyberattacks that it faces and this could be done by carrying out AI-enabled cyber exercises which in turn, could be made possible if a comprehensive and a robust piece of legislation is put into force. Data can be analysed with the help of AI in microsecond speeds with the help of AI speed deep analytics. It can be safely said that AI is reaching its zenith and with the advancements and the growth of the 5G network and the Internet of Things (IoT), AI has comparably become faster, comprehensive and much more robust as compared to what it was initially when it was developed. AI can be deemed to be regarded as a much faster approach when it comes to adhering to the process of risk management, since the traditional methods are obsolete and do not understand the essence of time. It is highly imperative to understand that a nation needs to make a law or a comprehensive piece of legislation which can be implemented de facto even when humans are not in the loop as this would lead to the development and the growth of an AI-enabled critical infrastructure.

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AI Ethics and Cybersecurity with special emphasis on a Strategic Partnership Approach With regards to strategic partnership, AI and cybersecurity can be divided into three sub-parts namely: a) AI, which can be deemed to be regarded as a part when it comes to the protection and security of critical infrastructures, for instance, the usage of telecommunication services, smart grids or the scrutiny of certain judicial or democratic operations; b) proper control over AI algorithms which do not jeopardize the way an algorithm works, for instance, protection over AI in order to ensure that no data is leaked or no data is hacked when it comes to the algorithm which is programmed into an intelligent device, such as a self-driving car; c) weaponization of AI, that is if AI is used in order to create biotech weapons, then these weapons should be programmed with utmost care, since the hacking of an algorithm which powers these weapons could lead to a potential cyber massacre. It is imperative to understand that having a strategic partnership with other countries can clearly be deemed to be regarded as a political matter and the countries which are capable of fostering such strategic relations need to come to an understanding, i.e. finding a middle or a common ground in order to ensure that both the parties benefit from such a strategic partnership whilst protecting their cyber-security. In this process, countries need to formulate their own pieces of legislation when it comes to AI and cyber-security, with special emphasis on AI and ethics, which would inherently mean that they need to adhere to develop a solid strategic relation or they need to have a combined sovereignty (How AI can be a force for good, 2018 pp. 751-752). AI and cybersecurity can be used for a range of purposes which could also be extremely offensive or destructive in nature, for instance, the kill-switches or the ‘logic bombs. Apart from this, potential AI cybersecurity threats and attack software’s for cyber-deterrence could also pose a huge threat (Deterrence and norms to foster stability in cyberspace, 2018 p. 323). In fact, it is interesting to note that such cyber-AI technologies could be used in order to make AI-powered weapons which could be potentially lethal and hazardous. With regards to the aspects revolving around AI and weaponization, Lethal Autonomous Weapons (LAWS) are being developed and there are a plethora of issues pertaining to the development of these Lethal Autonomous Weapons (Brundage, M., et al., 2018). In a nutshell, strategic partnerships revolving around AI and cybersecurity considers two important aspects. One of the aspects being, the strategic or the tactical use of AI ethics and the second aspect revolving around the ethics that concern AI-enabled cyber-weapons and AI enabled weaponization. In order to ensure that strategic partnerships work, it is imperative to understand the effects of developing an AI and ethics guidelines, which needs to be formulated

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by various countries and these guidelines need to be strictly adhered to in the international regime in order to ensure that a common good route is open. Countries need to specifically focus on bolstering their AI defence and security policies with special emphasis on the development and the regularization of weaponized AI. Countries who are members of the UN could perhaps raise the issues at the UN level and try to formulate an international treaty which specifically elucidates upon the non-participation in terms of AI cyber arms race and the non-proliferation of cyber-arms (UNIDIR 2017 emphasises on the various challenges pertaining to such aspects) (UNIDIR, 2017). AI Ethics and Cybersecurity with special emphasis on taking a Global Common Good Approach. With regards to taking the Global Common Good Approach, it is imperative to understand that a common good approach cannot be deemed to be regarded as something that is familiar to an internationally accepted terrain. In fact, in the year 1980 and the years that followed, there were a number of changes, massive changes to be precise which took place and these changes were deemed to be regarded as a dramatic global challenge. The dramatic global challenge was the growing hole in the ozone layer. A number of experts, scientists and policymakers of various countries joined hands and bolstered their resources in order to reduce the release of the toxic CFCs. CFCs can be deemed to be regarded as the chemicals which were potentially destroying the ozone layer. Furthermore, within the next two years, the Montreal Protocol was signed by various countries and this led to CFCs being banned, however, the effect that these CFCs had created lasted for quite some time, but eventually the ozone layer started to come to back its normalcy. This could be deemed to be regarded as a milestone achievement when it came to protecting or achieving a global common good. In fact, it is imperative to note that the Montreal Protocol rendered the sovereignty concerns to be put to checks, which in turn dealt specifically with the innumerable concerns with regards to the addressing the common good and dealing with the various precautionary principles (Lessons from the Montreal Protocol: Guidance for the next international climate change agreement, 2009 pp. 253-283). However, one of the major aspects that needs to be focused herein is whether cyberspace can be used in order to render its usage as a global common good and in what way can it be deemed to be in consonance to the provisions of International Law. Back when Internet came into existence, it was a “free, open, and a global service”, and as a matter of fact, a number of individuals, perhaps idealistic individuals wanted the internet to be available to all individuals across the globe for the usage of common good for all of humanity (Barlow, 1996). However, when the internet initially came into existence, it suffered from a

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number of cybersecurity threats which simply rendered it vulnerable in line with the cybersecurity threats that exist today. The Internet had no privacy regulations, neither did it have any security by design protections or regulations in place. However, the internet has come a long way since then and thanks to AI, all these loopholes have been corrected and it can be safely said that AI is a major contributor when it comes to protecting these technologies, such as the usage of block chain and encryption. It is extremely crucial to understand that the basis of considering cyberspace as a global common good is that there exists a “common ethics of global cyberspace”. In fact, during the stage of implementation, it is extremely imperative to focus on how cyberspace affects the principles of global ethics and how can it imply upon the aspects pertaining to the security-by design, privacy-by-design and autonomy-by-design and at the same time consider aspects pertaining to inclusivity. It can be said that this is a paradigm shift from the concept of “code is law” to “law is the code”. It is imperative to note that in the beginning of the year 2000, Lessig (Lessig, 2000; Lessig, 2006) elucidated upon the requirement of a technical infrastructure on the lines of regulating the internet and making sure that it falls under the ambit of the various legislative rules, i.e., “code is law.” However, the conundrum that lies in here is the fact that the Internet can today be deemed to be regarded as, “law is code”. This basically means that it is imperative to have an international community or a group of individual nations that come together and foster their relations in order to maintain some ethical guidelines which primarily relies upon the principles of hard law since the conditions in which we accept the usage of various technologies and allow it to be used in the market is vide the certification that it receives from the various regulating authorities. In the case of AI, much emphasis needs to be laid upon an open source code or a distributed control over the usage of AI in cyberspace. It is imperative to understand that it is extremely crucial to protect and consider the core aspects with regards to the usage of Internet, its domain name, thereby enabling it to be regarded as a source of public common good (Aligning the international protection of “the public core of the internet” with state sovereignty and national security, 2017 pp. 366-376). It is extremely necessary to understand that the global common good approach can be deemed to be regarded as an approach that is extremely non-state centric in nature. In fact, it has the ability to be deemed to be regarded as unrealistic, however, it is not unrealistic in nature. In fact, this approach has the capacity to render or allow various states to focus primarily on the scarce resources that it possesses with regards to the protection of its sovereign rights and duties or with regards to various other matters that cannot be deemed to be regarded in line with the objective of the global common good, for instance, the military objectives or the justice or the educational system objectives. It can be said that

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due to the non-adherence to the objective of global common good in the case of cyberspace has led to governance on an international level to be deemed as vulnerable and such international interest governance cannot be deemed to be regarded as something which is properly equipped. It is crucial to understand that any sort of international governance can be deemed to be regarded as risky when it comes to taking a state-centric approach with regards to the usage of cyberspace and the laws concerning it, furthermore, there is a lot of ambiguity with regards to how international governance and international laws may pan out. However, a number of opinions have been laid down by (Cowhey, et al., 2017) and there are a number of grounds which lay the foundation for the development of AI in cybersecurity. For instance, the initiatives taken by the United Nations which are in consonance to AI and Cybersecurity can be deemed to be regarded as something which are aimed towards the internet community and there are other initiatives which emphasize specifically on certain related subjects such as the ICANN as well as the Web Foundation’s #ForTheWeb movement. Apart from this, there are a number of SOLID technical implementation programmes which are carried out which focus primarily on the various aspects revolving around the implementation of data protection laws, furthermore taking into consideration the various aspects of doing business globally along with the various revolving around the Internet of Things (IoT). It is quite interesting to note that there are a number of UN cybersecurity norms, rules, regulations, principles which need to be adhered or taken into consideration, in fact, CBMs is the cyberspace do not specifically pay heed towards the achievement of a global common good and instead focus majorly on the aspects revolving around risk management. These aspects can be deemed to be regarded as extremely remarkable considering its scope, as the United Nation’s conjecture to remit the global good for all principle has a deep rooted foundation when it comes to contributing a global common good in a number of other areas. However, there do exist a number of weak points between the work carried out by the UN on a global front- one of the most recent aspects can be deemed to be regarded as limited to the UN GGE Report, which was published in the year 2015 and delves into the aspects pertaining to global common good with special emphasis on AI and cybersecurity laws on an international front. Furthermore, the commitments which have been laid down in the UN GGE Report enumerate upon the norms and principles level, however, they do not enumerate upon the CBM level. The commitments which have been put forth in the UN General Assembly focus majorly on creating an open, peaceful internet, which has the protection of international peace and security, thereby also adhering to the principles of humanity and consistency as enumerated under the UN Charter. The Report which was published in 2015 also focused upon how various countries can bolster their resources in order to come together and carry out other practical work, i.e. the development of

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CBMs, the development of a common ground when it comes to addressing the various aspects pertaining to how the provisions of international law may be applicable when it comes to creating a peaceful information technology centre and how these aspects can be used in order to create a peaceful environment on the cyberspace, whilst making use of AI tools and Machine Learning applications, i.e. how can all these aspects be taken into consideration at a technical, legal or a policy level. In a nutshell, it is imperative to understand that a global common good approach needs to be given much emphasis when it comes to dealing with the various ethical and legal aspects pertaining to strategic autonomy in the field of AI and Cybersecurity laws. It can be said that a comprehensive or a robust plan in this regards can be adhered to, when it comes to addressing the self-interest of states with regards to their sovereignty and also in the interest of global business, since it has a long run tradition and on an international level, the UN has the capacity to provide with the necessary assistance in terms of the political support, and the work that it may carry out within the private sector or with the help of the corporations in the private sector, the internet community and lastly the civil society. An appeal can be made in order to foster the private-public relations when it comes to adhering to a common good. For instance, intergovernmental work in association with the UN can go a long way in promoting a global standard for AI and ethics in the cyberspace sector. Furthermore, specific focus can be laid upon the aspects pertaining to the various states or the alliances that various states have with each other, when it comes to going ahead and reaching to a consensus with regards to the achievement of a global common good in the AI and ethics aspect in cyberspace. States can further bolster their resources in order to create alliances. For instance, the EU, can promote and source AI and the various security measures pertaining to the usage of AI, in order to ensure that there is a widespread security control and they could further allocate a budget for Research and Development and focus on the various investment opportunities when it comes to developing the AI and ML tools in cyberspace, at the same time focusing on the requisite provisions of the law. Lastly, it is imperative to throw light upon one of the major conundrums that play a significant role when it comes to ensuring that the measures to reach a global common good are adhered to or not. It is necessary to go ahead and ensure that there is a proper or a rather comprehensive interplay between the usage of AI within the cybersecurity regime, focusing primarily on the aspects pertaining to personal data protection. The EU has the GDPR and in terms of EU, the application of AI when it comes to the usage of personal data can be used in order to clarify the relationship that exists between the various principles of national security and the transparency with regards to the way decisions are

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taken, basically with regards to the aspects revolving around automated decision making. It is imperative to take into consideration the interplay that exists between cybersecurity, AI and ethics in consonance to the sovereignty of a nation. Sovereignty can be deemed to be linked with the various approaches that have been enumerated above, for instance the approach that it takes to strategic autonomy and how these aspects pertain to ethics. Ethics can be deemed to be regarded as a set of rules which are made by various states and it specifically emphasises on the various notions pertaining to, “code conditions law” and vice versa. However, a nexus can be drawn between AI and cybersecurity since both focus majorly upon, “intelligence” and actors in a state can be deemed to be regarded as intelligent, regardless of the fact if they are state actors, nonstate actors or virtual actors. It is imperative to note that cybersecurity and ethics in consonance to the aspect of sovereignty can be deemed to be regarded as an ethical outcome which exists due to the sovereignty gap, for instance, how can one determine the threshold of damage that can be caused due to the existence of a sovereignty gap, if the aspect of sovereignty gap becomes an important aspect. Basically, when can a state’s legitimacy be deemed to be regarded as jeopardized? A nexus can be drawn here taking into consideration the ethical aspects pertaining to state legitimacy (Hurrell, et al., 2012). The legitimacy which is internal can be deemed to be regarded as legitimacy which accounts to the power and authority, which means that it can be either accepted or given consent to, or it may pertain to the institutions that are being run by the citizens of a particular state or a particular nation (Biersteker, 2012). It is extremely necessary to understand that when the damage is caused due to the aspects pertaining to “cybersecurity”, the authorities of a country or of a nation can be rendered vulnerable and cyber security can potentially be deemed to be regarded as fragile. These issues can further lead to undermining the external confidence of a nation or it shakes the core of a nation if it is a nation which is a de facto subordinate to a powerful and a global organization, for instance, companies which provide an AI platform. Ethics can then be deemed to be regarded as coming a step closer to sovereignty, which can then be deemed to be regarded as a right which also takes into ambit the rights pertaining to selfdetermination. This further leads to the argument with regards to whether the cost of maintaining the sovereignty of a state can be equivalent to the human rights of a state which is another important aspect that needs to be given due regards to. The aspects pertaining to the human rights of a nation can be an important aspect when taken under the garb of cybersecurity within the UN. The cost pertaining to the legitimacy and the validity of human rights can be deemed to be regarded as a preconceived notion that draws a nexus between cyber and ethics in consonance to sovereignty. Cyberspace raises these costs and issues and the question that is imperative to be addressed here is whether

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the costs pertaining to the creation of a sovereignty gap are acceptable and whether they cost any damage to the lives of individuals, for instance, not getting timely medical assistance (Wannacry Incident) or when it comes to the infringement of the various freedoms that an individual is ought to face, for example, the freedom of expression. In a nutshell, there arise three aspects when it comes to addressing the various issues pertaining to the usage of AI in cybersecurity. 1) the state and the nonstate actors can be deemed to be regarded as intelligent; 2) the sovereign gap that is created due to the non-existence of a robust piece of legislation leads to the occurrence of a heavy cost; 3) the conditions with regards to the incurring of the costs; and 4) how the code conditions law and how the law conditions the code. The major aspect that arises here is whether internal and external state legitimacy can be accepted and whether it can be deemed to be regarded as a well-known notion pertaining to a sovereign political based theory. The legitimacy of the state can be deemed to be regarded as something that is contestable by the usage of intelligent actors of the state. Furthermore, the maintenance or the adherence with regards to the legitimacy of a state can be deemed to be regarded as having a cost and the legitimacy of the state can be based or adhered to on the basis of technology, while technology can be based on the conditions pertaining to state legitimacy. However, with regards to all the challenges reiterated above, it can safely be said that AI and cybersecurity laws need to be made robust and a further emphasis needs to be laid upon the provisions pertaining to AI Ethics and the legitimacy of the State.

Case Studies The main aim of having a special section on case studies is to ensure how the various international organizations and certain private players have shaped their AI policies in consonance to the Cybersecurity laws. Risk Management can be an important element when it comes to taking into account the various aspects with regards to the development of AI and cybersecurity and the monitoring of the behaviour of a state. • The UN Governmental Group of Experts are working towards making a comprehensive set of principles which would ensure stability, restraint, transparency, mutual responsiveness and compatible governance (Heinl, 2019; Timmers, 2019); • A number of private and public initiatives have been made for instance, the Paris Call for Trust and Security in the Cyberspace, and these consist of norms which are extremely crucial when it comes to dealing with cybersecurity with the usage of AI. It is imperative to understand that norms

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can be deemed to be regarded as comprehensive and robust with the help of Confidence Building Measures (CBMs). Confidence building measures or CBMS are made with a view in order to ensure that countries do not aim towards attacking each other’s critical infrastructures, rather they work together in order to create a joint cyber exercise. An important example that can be cited here is for the governments of various countries to work with each other in close consonance on developing a CBM which specifically elucidates upon cyber-resilience in the health sector along with fostering relations with the WHO. It may seem that the implementation of CBM’s could be the way forward, however, in reality it is quite difficult to implement cyber CBM’s since they are deemed to be regarded as successful and operational only when strategic autonomy is used as an approach to tackle the multitude of issues surrounding AI and cybersecurity. For instance, cyber CBM’s would work successfully under the Global Alliance against Child Abuse and when it comes to the development and training of law enforcement; • Strategic Partnership Approach primarily takes under its ambit economical, democratic and societal aspects and in consonance to these aspects, Germany recently came up with an idea to propose the development of an, “Europeans-only cloud, GAIA-X”; • A high-level group working on AI and ethics with special emphasis on cybersecurity laws was created by the European Commission recently and they proposed certain guidelines on AI and ethics (European Commission, 2019). Reliance upon such guidelines could be deemed to be regarded as extremely crucial when it comes to dealing with the various aspects surrounding strategic partnerships between nations. It is imperative to note that these guidelines are somewhat on the similar lines as compared to the personal data protection bill and the laws pertaining to the European Union, i.e., the General Data Protection Regulation (GDPR); AI Applications in Cybersecurity along with special emphasis on Real Life Examples It is extremely crucial to understand that Machine Learning has the capacity to analyse and collate billions of data packets in seconds with the help of modern statistical tools. A number of organizations have the capacity and the ability to scrutinize and go through billions of data packets and therefore AI applications play a very prominent role in Cybersecurity. • Security Screening: The United States Department of Homeland Security has created an AI powered system which is known as the, “AVATAR”. This system has the capacity to recognize body movements and the expressions

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that an individual makes. AVATAR relies on AI algorithms and Big Data, when it comes to analysing the expressions of an individual which thereby enables the officers to scrutinize what a potential criminal could behave like. Now, security screening is usually carried out by immigration officers and the officers who work in the customs department, which enables them to identify whether an individual is being truthful or not or whether he has certain ulterior motives. This system has been extremely useful for the law enforcement agencies since this system also has the capacity to identify and monitor the changes in the voice of an individual thereby alerting the law enforcement agencies that a particular individual could pose a potential threat. • Crime Prevention: The police department of New York city uses an AI powered system called the CompStat or The Computer Statistics, which is a relatively older form of AI, which was developed when AI was relatively new and was being pondered upon. The CompStat has the capacity to carry out management activities and it is reliant on various software tools, which enables it to do so. The system could be deemed to be regarded as the first tool which was used by the New York Police Department in order to “predict policy” and thereafter a number of police stations across the territorial boundaries of the United States have been using CompStat as a means to carry out investigation. There are further AI-based crime analysis tools such as the California based Armorway which adheres on the usage of AI and game theory when it comes to understanding or predicting security or potential terrorist attacks. Coast Guards also rely heavily upon Armorway when it comes to securing the ports in the territorial limits of Los Angeles, Boston and New York. • Analysis of Mobile Endpoints: Mobile endpoint threats could be determined with the use of AI and Google has been striving to develop an AI powered system which has the capacity to identify mobile endpoint threats. A multitude of organizations have the capacity to take benefits from this service which is offered by Google in order to protect the growing number of personal mobile devices. In fact, companies such as Zimperium and MobileIron have decided to join hands and create an anti-malware software for mobiles which would be powered by an AI algorithm. The AI based threat detection software which Zimperium is working upon would have the capacity to address a multitude of issues such as, issues pertaining to network, device and the various threats that may loom over the use of an application. • AI Powered Threat Detection: ED&F Man Holdings is an organization which works in the field of Finance and in particular is a commodity trading firm. This organization faced a cyber-security breach a few years ago. After

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scrutiny, the firm realised that it needs to work upon improving its cybersecurity processes and tools (Altexsoft.com). The organization worked with Cognito, which is Vectra’s AI based threat detection software. Now, it is interesting to note that Cognito has the capacity to store metadata and also recommends various security measures that an organization needs to take or adhere to in order to make its system more comprehensive and robust. It has the capacity to use metadata along with the techniques pertaining to Machine Learning which enables an individual to analyse and detect potential cyber-attacks which may occur in real time. Cognito was deemed to be regarded as extremely useful for ED&F Man Holdings since it enabled the organization to put brakes on to a multiple-man-in-the-middle attack and also enabled it to put a full stop on a crypto mining scheme which was being undertaken in the Asian subcontinent.

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International Telecommunication Law

Abhivardhan, Sameer Samal and Aditi Sharma

Introduction The chapter is intended to cover the role of the telecommunication industry in democratizing and socializing AI & how the International Telecommunication Union generally works. The authors have affirmatively discussed the legal basis of international telecommunication law, its policy trajectories, the conjunction of the field of ITL with AI Ethics considering AI as an Industry, as in most of the chapters, and how does this stand out in general. To understand the deeper role of technology-related aspects of international law, please refer to the Section on AI and Digital Studies, and the chapter on International Human Rights Law.

Legal Background The International Telecommunication Union is a follow-up to an already defunct international body, namely the International Telegraph Union. On January 1, 1949, the Organization’s main international law treaty, i.e., International Telecommunication Convention of 1947, as a follow up to the 1865 Convention on international telegraph networks per se. In the 1992 Conference, 4 sectors were established to govern international telecommunication law with clarity and some scope of review cum enforcement. Telecommunication policy is at the heart of the information age, and the following sectors have utmost importance in the realm of international law till date despite the emergence of the 5th generation warfare: • • • •

Radio communication (ITU-R) Standardization (ITU-T) Development (ITU-D) ITU Telecom

ITU-R, also known as CCIR or the International Radio Consultative Committee earlier, is an old sector in matters related to governing radio communication, which manages the international radio-frequency spectrum and satellite orbit resources. Established in 1956 as the International Telephone

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and Telegraph Consultative Committee or CCITT, ITU-T is an important sector, because other than radio telecom, this sector handles the standardization of all other telecommunication technologies. It also includes a significant set of 2 groups, which directly handle considerations on AI Policy: • The ITU-T Focus Group on Machine Learning for Future Networks including 5G is the first one, which was created through the ITU-T Study Group 13 at its meeting in Geneva, 6-17 November 2017. Interestingly, the group establishes in their work on architectural frameworks central to machine learning the following challenges to such an integration (International Telecommunication Union, 2019 p. 4): ─ The heterogeneous nature of ML functionalities and unique characteristics of future communication technologies impose a varied set of requirements for integration; ─ Roadmaps for evolution of these ML functionalities and communication networks are not aligned; ─ The cost of integration, in terms of architecture impact; ─ Disparate management mechanisms for ML functionalities and network functions to disrupt the operations management of communication networks; Even in the document related to data handling to enable machine learning in future networks, the important challenges, which have been addressed are mentioned as follows (International Telecommunication Union, 2020 p. 4): ─ diversity in network data sources; ─ increased flexibility and agility of future networks, rendering complications; ─ evolution of networking techniques leading to multiplicity of applicable network configuration parameters and policies; • The second group of utmost importance in the arena of AI and Law is the ITU/WHO Focus Group on artificial intelligence for health (FG-AI4H), in collaboration with the World Health Organization. This Focus Group is open for free, and is a central group to represent concerns and to coordinate for policy discussions over the relationship and usefulness of AI in the field of healthcare; ITU-D since 1992 has established a Secretariat for the Broadband Commission for Sustainable Development and contributed reasonably into endorsing efforts for equitable, sustainable and affordable access to information and communication technologies (ICTs), while the ITU Telecom is responsible for organizing various events in connection with the ICT community.

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Agenda for the Information Telecommunication Union, 2005)

Society

(International

In the Tunis Agenda of 2005, the development cum recognition of ICTs was properly addressed, and the basis of democratizing internet among the members of the ITU was laid in place. The important aspects of the Agenda were as follows: • Countries agreed to acknowledge the pre-existent digital divide and the emergence of internet, and also emphasized upon the need to empower the developing countries for more funding needs to be put in action; • Scaling the ICTs was made a real possibility and affirmative through this and so corporate social responsibility became an important aspect behind democratizing the use of ICTs around the world14;

The agenda document as enumerates in clause 23: [W]e recognize that there are a number of areas in need of greater financial resources and where current approaches to ICT for development financing have devoted insufficient attention to date. These include: a. ICT capacity-building programmes, materials, tools, educational funding and specialized training initiatives, especially for regulators and other public-sector employees and organizations. b. Communications access and connectivity for ICT services and applications in remote rural areas, Small Island Developing States, Landlocked Developing Countries and other locations presenting unique technological and market challenges. c. Regional backbone infrastructure, regional networks, Network Access Points and related regional projects, to link networks across borders and in economically disadvantaged regions which may require coordinated policies including legal, regulatory and financial frameworks, and seed financing, and would benefit from sharing experiences and best practices. d. Broadband capacity to facilitate the delivery of a broader range of services and applications, promote investment and provide Internet access at affordable prices to both existing and new users. e. Coordinated assistance, as appropriate, for countries referred to in paragraph 16 of the Geneva Declaration of Principles, particularly Least Developed Countries and Small Island Developing States, in order to improve effectiveness and to lower transaction costs associated with the delivery of international donor support. f. ICT applications and content aimed at the integration of ICTs into the implementation of poverty eradication strategies and in sector programmes, particularly in health, education, agriculture and the environment. (International Telecommunication Union, 2005). 14

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Constitution and Convention of the International Telecommunication adopted by the 2018 Plenipotentiary Conference

ITU Constitution. The Constitution of ITU is the basic charter that establishes how the International Telecommunication Union is governed. The following chapters of the Constitution are crucial for reasonable understanding and critical analysis:

Chapter II, Article 12.

This chapter covers the governance of the Radiocommunication sector in line with the Article 44 of the Constitution. Article 12 states it clearly that the chapter’s scope is entailed within (a) “ensuring the rational, equitable, efficient and economical use of the radio-frequency spectrum”; and carrying out studies and offering recommendations on matters related to radiocommunication.

Chapter III, Article 17.

This chapter covers governing the telecommunication standardization sector. In Article 17, a sense of centrality of concern vide Article 1 of the Constitution has been given to the developing countries, and reckoning the roles of various bureaus and sub-bodies in telecom standardization has been certified prima facie.

Article 22.

This article is dedicated to emphasize upon the role and conduct of telecommunication development conferences (TDCs). Between two Plenipotentiary Conferences, one world telecommunication development conference & (subject to resources and priorities), some regional telecommunication development conferences can be organized. TDCs do not produce final acts, but resolutions, decisions, recommendations or reports.

Chapter VI.

This chapter recognizes some basic principles of international telecommunication law in a public context. The chapter includes: • Article 33: Right of the Public to Use the International Telecommunication Service; • Article 34: Stoppage of Telecommunications subject to public order and decency; • Article 35: Suspension of international telecommunication services following which a notification to the Secretary-General has to be provided; • Article 36: Member States accept no responsibility towards users of the international telecommunication services, particularly as regards claims for damages; • Article 37: Member States agree as per the ITU Constitution to ensure the secrecy of international correspondence. They can notify competent authorities as circumstances lie; • Article 38: Establishment, Operation and Protection of Telecommunication Channels and Installations;

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• Article 39: Notification of Infringements; • Article 40: International telecommunication services must give absolute priority to all telecommunications concerning safety of life, whether anywhere – sea, land, air or even outer space; • Article 41: Subject to the provisions of Articles 40 and 46 of this Constitution, government telecommunications enjoy priority over other telecommunications “to the extent practicable upon specific request by the originator”;

Article 56 and Arbitration Procedure. Article 56 of the ITU Constitution empowers the provisions of the ITU Convention over utilizing arbitration between member-states in case any dispute between them remains unresolved. Chapter VI of the Convention deals with the process of arbitration enumerated as follows: • The parties shall decide by agreement whether the arbitration is to be entrusted to individuals, administrations or governments. If within one month after notice of submission of the dispute to arbitration, the parties have been unable to agree upon this point, the arbitration shall be entrusted to governments; • The parties to the dispute may agree to have their dispute settled by a single arbitrator appointed by agreement; or alternatively, each party may nominate an arbitrator, and request the Secretary-General to draw lots to decide which of the persons so nominated is to act as the single arbitrator;

Important Resolutions. The important resolutions with regards to the ITU are enumerated as follows: Strengthening the regional presence. • Resolution 25 (2018) (International Telecommunication Union, 2018) vide Resolution 17 recognizes the need to have a regional presence of the organization in various places, with their advisories, groups and boards to be present in a coordinating fashion; Request to the International Court of Justice for advisory opinions.

• Resolution 59 (1994) (International Telecommunication Union, 1994) was a part of the Plenipotentiary Conference in Kyoto, Japan, where noting “to affiliate the Union to the Administrative Tribunal of the International Labour Organisation”, the ITU’s Council can refer advisory opinions to the International Court of Justice according to the Article VII of the Agreement between the ITU and the United Nations;

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Juridical status. Resolution 60 (1994) (International Telecommunication Union, 1994) was a part of the Plenipotentiary Conference of Kyoto as well – which recognizes that the privileges and immunities of the ITU are equivalent to those obtained by other “organizations of the United Nations family”; ITU’s role with regard to international public policy issues pertaining to the Internet and the management of Internet resources, including domain names and addresses.

• Resolution 102 (2018) (International Telecommunication Union, 2018) in general, calls for promoting and offering technical assistance to developing countries & protect multilingualism on the internet in various countries as well for an integrating and inclusive information society while recognizing the role of private enterprises in telecommunication. The resolution also instructs the ITU Secretary-General to, in line with § 78 a) of the Tunis Agenda, continue to contribute as appropriate to the work of IGF; Measuring information and communication technologies to build an integrating and inclusive information society.

• Resolution 131 (2018) (International Telecommunication Union, 2018) recognizes that “the ICT Price Basket (IPB) and the ICT Development Index (IDI) are important for measuring the information society and extent of the digital divide in international comparisons”, and instructs the ITU Secretary-General and the Director of the Telecommunication Development Bureau to “ensure that projects, while having highly different goals and scopes, take account of the data, indicators and indices for measuring telecommunications/ICTs for their comparative analysis and for measurement of their results”; Role of administrations of Member States in the management of internationalized (multilingual) domain names.

• Resolution 133 (2018) (International Telecommunication Union, 2018) makes the ITU aware that there should be a continuing commitment to working earnestly towards multilingualization of the Internet. The resolution also invites Member States and Sector Members “to take an active part in all international discussions and initiatives on the further development and deployment of IDNs”; Deployment of future networks in developing countries.

• Resolution 137 (2018) (International Telecommunication Union, 2018) takes into account the need to transit from smooth transition from existing

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networks to future networks for the developing countries. Under the ITU Study Group 13, and their established Focus Group on "Technologies for Network 2030" (FG NET-2030), invites the stakeholders to focus on the implementation of future networks, especially in regard to future networks planning, deployment, operation and maintenance, and the development of NGN-based applications, especially for rural and remote areas; Conformance and interoperability.

• Resolution 177 (2018) (International Telecommunication Union, 2018) recognizes the inexplicable role of technical training and institutional capacity building for testing and conformity to promote global connectivity. The resolution invites the ITU member-states to “to work together to combat counterfeit equipment using nationally and/or regionally established conformance assessment systems”; The role of telecommunications/information and communication technologies in regard to climate change and the protection of the environment.

• Resolution 182 (2014) (International Telecommunication Union, 2014) addresses the issue of climate change in consideration with “promoting innovative and sustainable development activities presenting relatively low risk to the environment”; Facilitating digital inclusion initiatives for indigenous peoples.

• Resolution 184 (2010) (International Telecommunication Union, 2010) is a special resolution in line with the Article 16 of the United Nations Declaration on the Rights of Indigenous Peoples in line with Resolutions 46 (Doha, 2006) and 68 (Hyderabad, 2010) to endorse digital inclusion and more participation of indigenous peoples;

Conjunction with Artificial Intelligence Network optimization Automation • Telecom service providers require a proper end-to-end visibility, so that they can manage and optimize network performance. However, this implies a wide range of service requirements. For example, to satisfy the network requirement, a high throughput broadband network and low latency network configuration is necessary. It means the task to make appropriate decision and use technological solutions would be very challenging. If redesigning of

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network is done manually, it is very time consuming. One approach to cater the problem is network slicing that can satisfy diverse requirements. AI and machine learning provides solution by translating service requirements of use cases to network requirements, automatically. It builds SONs that provides operators ability to optimize networks on the basis of traffic information. Reports suggests that 63.5% of operators are investing in AI systems to improve their infrastructure. (GURNANEY, 2017) Companies like Nokia are launching their own machine learning-based platform that provides network management solutions. Nokia AVA is a cloud-based program that offers data science, telco and cloud expertise from both Nokia and Microsoft. (NOKIA) • The benefits of using this technology is uncountable. Essentially, it simplifies data wrangling process and accelerate time for complex customer, billing and network data. It also allows non-designers to access data from all sources, lessening the burden of IT department for ongoing analytics support. n. Through automating operations, implementing both end-to-end service orchestration and a domain Mediation, Abstraction, Orchestration layer, and using Artificial Intelligence and Machine Learning engines, communication service providers are enabling their digital transformation journey towards digital service providers. The automation of engineering and operation is aiming to increase consistency, accuracy and efficiency, leading to costeffective and customer benefit networking. (DELOITTE) Cognitive heterogeneous networks and ML-based SON • The service providers across the globe have been putting efforts to bring down capital and operational expenditure, while at the same time, bring optimal solutions for customer problems. One of the strategy is to automate parameters through self-organizing network (SON). (LOHMULLE, 2020) International Mobile Telecommunication (IMT)-2020 requires a standard network that can provide services to support diverse customer requirements through network function instantiated as appropriate. (2019) Currently, the network uses flexible automated systems like self-organizing networks. However, requirements for future are not high data rates but smart and optimal network that can keep all aspects of telecom, users, services and machines, connected. The current common SON solution runs on three aspects – self-optimization, self-configuration and self-healing. (PENG, et al., 2020) However, SON enabled networks are still far from realizing a network that is autonomous and self-managed as a whole. In fact, the behavior of the SON functions depends on the parameters of their algorithm, as well as on the network environment where it is deployed.

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Therefore, a new network, cognitive heterogeneous network are built based on artificial intelligence techs that allows networks to be more aware about its environment, network problems, user behavior, and much more. • In this new network, the self-configuration would include plug and play configuration for any new deployed radio access nodes that can configure its identity, power and transmission frequency. It can lead to faster cell planning and rollout. Similarly, self-optimization would mean optimization of capacity, coverage, interference and handover. And, self-healing would include automatic detection of failures and thereby its removal, and adjustment of parameters automatically. Therefore, a machine learningbased SON can monitor network alarms and takes proper action and give recommendations for network designs, without any human intervention. (Online Learning for Energy Saving and Interference Coordination in HetNets, 2019) ML-enabled networks would support the probing and monitoring systems that are connected to network element, so that it can get accurate results for transporting KPIs. It would also support an SON framework, that is connected to a centralized performance monitoring system aims to collect and consolidate data from all network management systems and probes. (DAHER, 2018) AI and Predictive maintenance • AI and machine learning has capacity to make fault prediction by analyzing trends, anomalies and causalities like change in volume of alarms, or peergroup entities. In order to achieve closed loop automation, machine learning and AI is expected to support fault management, cause root analysis and automated recovery. Fault management systems promptly detect failures that cause unstable behaviors before it escalates to critical failure. AI processes huge volumes of management data for the same. Similarly, for root cause analysis, AI requires to identify the type of failure, its location and mapping information of automatic functioning. It identifies root cause based on previous experiences obtained from actual and test environments. (2019) • Predictive failure recognition can be extended beyond the network element level to making prediction of an end-to-end service availability. It means, the operators can use insights based on data, monitor state of equipment, anticipate failures, and fix problems through communication hardware. (CRAWSHAW) This helps the operator to avoid penalties or customer agitation by predicting service key performance indicator, over time. AI can enable and track thousands of key performance indicators while discovering abnormal behavior.

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• However, mere prediction is not sufficient. It raises essential questions like when a prediction is made, should the manager remove associate element instantly, or find a solution to it, or fix it, or scramble it, or wait till the failure occurs. All these situations have its own pros and cons. In short term, this network automation and AI would be able to enable root-cause analysis and predict issues. In long term, however, the technology will strengthen strategic goals like dealing with emerging business need and creating customer experience. In addition to predictive maintenance, AI is also used in preventive maintenance, which is effective to both network operators and customers. In case of potential service-level objective violations, it notifies the operators so that preventive maintenance may be undertaken. (2020) In general, the sustainability of fault recovery process depends on the implementation of workflows that re predefined. Therefore, one of the expected roles of AI and machine learning is to reduce the exceptional conditions by improving workflow continuously.

Case Studies 5G Technology and Telecommunication Laws Artificial Intelligence has the capacity to transform with the aid of advanced telecommunication such as 5G technology. Internet of Things and other similar technologies can reap the benefits of such high-end communication channels and develop into better systems. 5G technology has the potential to radically change the way humans interact and communicate with each other as well as with technology. This is possible due to the law latency, substantive growth in system capacity, ultra-high reliability and gigabit speed.

This technology has a lot of potential and can be used in the following cases: -

Autonomous vehicles Smart Factories Smart Cities Augmented Reality Remote Healthcare Facilities 3D Video Platforms

To reap the most benefits telecommunication companies in the private sector, as well as the government need to regulate this technology in such a manner so as to foster growth and development. It is predicted that the 5G infrastructure will cost approximately USD 200bn every year globally (CMS, 2021). This

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shows how significant the price is already for telecom owners and carriers, which are increasingly requiring third party funding. Therefore, it is also important to consider the overall end-price that the consumer have to pay for this technology and its impact over the poorer nations that might need the services of certain essential industries, such as healthcare.

The characteristics of 5G network are completely technically different from the previous generations of telecom services. Despite the challenges faced, such as training and high price, the use of this technology is imminent as is its use in IoT technologies.

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Section 4: AI & Ecology

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International Environmental Law

Manohar Samal

Introduction The environment is a combination of living and non- living things which have formed naturally that depend upon a successful relationship and interaction with human beings. It consists of the lithosphere, hydrosphere, atmosphere and biosphere. The lithosphere comprises the outermost layer of the Earth which is a solid, rocky and a rigid upper mantle. Hydrosphere refers to all the water bodies and water elements present above the ground, below the ground and in the air. All the gases on Earth fall under the category of atmosphere and the biosphere includes all living beings on the planet within its ambit. Artificial intelligence technology has been deployed in every sphere including the environment. It’s paramountcy in transforming human interaction with natural resources, biodiversity, land use, climate change and safeguarding ocean spaces has led to possibilities of exploration and implementation in new frontiers that will ultimately help in achieving development which is environmentally sound and sustainable. In order for this to happen, it is undeniable that the correct approach in international law has to be employed so that the entire global society of nations are enabled to participate in the synthesis between technology and environment. Thus, in light of the above, this chapter has been dedicated towards discussing the close nexus between artificial intelligence, the natural environment and international environmental law which will show the evolution and advancements done in the field and also aid in highlighting the deficiencies.

Legal Introduction International environmental law possesses a wide embracing scope and extends over to public international law as well as private international law. The traditional principles have advocated that international environmental law strictly belongs to the public international law sphere. However, this is untrue in today’s context since open global economies with free investments have changed the way environmental initiatives can be visualised. Transboundary conservation and transformation efforts from States, international stakeholders

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and individuals have enabled private international law to apply to international environmental law as well.

Public International Law Customary Law. International environmental law as a subject of public international law includes its nexus with both, customary law, as well as conventional law. In the present context, customary international law embraces the environmental practices adopted and practiced by nations for long durations which has led to a valid source of international environmental law (Mathur, 2016). Despite the fact that various legal instruments pertaining to the environment already exist in the global sphere, lackadaisical implementation has resulted in exhibited significance of customary environmental principles (Dupuy, 2018). Moreover, the fact that sovereignty holds utmost importance in conventional instruments and the fact that only nations which ratify such instruments are bound by it, could make customary law a better option in the field of international environment (Dupuy, 2018). In fact, a school of thought has also advocated for international environmental law to be included within the ambit of jus cogens creating erga omnes obligations for nations (Robinson, 2018). The most commonly accepted principles under international environmental law are principle of sovereignty and responsibility, principle of good neighborliness and international cooperation, principle of preventive action, precautionary principle, polluter pays principle, principle of common but differentiated responsibility and the principle of sustainable development which also encompasses inter- generational and intra- generational equity under its ambit (Soto, 1996). Over the span of time, these principles have also been incorporated in international environmental conventional instruments. The principle of sovereignty and responsibility envisage that all nations have complete sovereignty over the natural resources available in their territory, but at the same time, have to responsibly utilise them in a manner which does not cause harm to the environment (Soto, 1996). This principle has been used in a plethora of international cases, most prominently, in the Trail Smelter case. The principles of good neighborliness and international cooperation and the principle of preventive action stipulate that nations should prohibit activities within its territories which are capable of causing environmental harm in other nations and in case of any environmental accidents or hazards, should show utmost cooperation in reducing the impact or remedying the hazards of such

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environmental harm (Soto, 1996). This principle was successfully resorted to by the International Court of Justice in the Costa Rica and Nicaragua Case. The precautionary principle was introduced during the Rio Declaration and states that all precautions to be taken during indulging in innovations and scientific uncertainty should not be used as an excuse to postponing cost- efficient measures to restrain environmental deterioration (Soto, 1996). The polluter pays principle revolves around compensation for the harm caused by the polluting country, institution or individual and the principle of common but differentiated responsibility is centered around the compliance of international obligations by nations with different responsibilities of environment conservation (Soto, 1996). The principle of sustainable development originated in the Brundtland Report and refers to development meeting the essential needs of the poor in line with all forms of economic, political and social development that is done in an environmentally sound manner which will help in preserving inter- generational and intra- generational equity of resources (Soto, 1996). International Legal Instruments. There seem to be a colossal number of international instruments in the form of conventions, treaties, agreements and declarations conferring environmental obligations over nations at multilateral, bilateral as well as regional levels. Few of the most prominent instruments include the United Nations Convention on the Law of Seas 1982, Geneva Convention on Long Range Transboundary Air Pollution 1979, Vienna Convention for the Protection of the Ozone Layer 1985, Kyoto Protocol 1997, Paris Agreement 2016, Helsinki Convention 1992, Rotterdam Convention 1998, Basel Convention 1989, Stockholm Convention 2001, Convention on Biological Diversity 1992, Ramsar Convention 1971, Convention on International Trade in Endangered Species of Wild Fauna and Flora 1973 and the Bonn Convention 1979. Despite the fact that a plethora of international instruments exist, implementation in the international environment sphere has been filled with lacunae. This is mainly due to lack of binding mechanism, dependence of the instrument upon country ratification and delay in enacting national level legislation by nations to implement their conventional obligations in the municipal sphere (Verma, 2012). Private International Law Increasing influence of private contracts and conduct in the environmental sphere has led to international environmental law to be associated with private international law as well (Bodansky, 2008). Increased globalized trade and exploitation of natural resources has led to various non- state stakeholders from different nations participating. All of these have resulted in dilemmas of private

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international law such as applicable jurisdiction, choice of law governing the contractual relationships and nations which have been empowered to enforce the same. In fact, dispute resolution mechanisms have also been progressively developed and take place right from domestic courts and forums to international forums such as the International Court of Justice in international environmental law cases.

Conjunction with Artificial Intelligence Artificial intelligence technology has been enabling environmental outcomes to achieve sustainable and orderly development and is not a recent or newfound phenomenon (Vinuesa, 2020). The usage of artificial intelligence technology can be beneficial in the spheres of climate change, biodiversity conservation, healthy oceans, clean air, water security, weather and disaster resilience (Herweijer and Waughray, 2018). The possibilities of such wide range application has been explored since the 1940s and has continued with ameliorating transformations and evolutions with the use of automated intelligence, assisted intelligence, augmented intelligence and autonomous intelligence (Herweijer and Waughray, 2018). The transformation has been continuous due to the improvement of big data, processing power, algorithms, connectivity and open software and data streams (Herweijer and Waughray, 2018). The aforesaid discussions make it pristinely clear that artificial intelligence can be deployed for environmental conservation, environmental monitoring and enforcement, environmental compliance and environmental damage repair.

Case Studies This section of the chapter is dedicated towards analyzing case studies of artificial intelligence applications in environment conservation, natural resources conservation, biodiversity conservation, green infrastructure creation, green jobs creation, increase in green commercial activities, climate change efforts, conservation of oceans and tackling natural disasters which will help in deriving the intended results.

Natural Resources and Biodiversity Conservation Biodiversity Conservation.

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Poaching has led to severe constraints in biodiversity conservation and therefore, various artificially intelligent enabled tools have been dispatched in the sphere. One prominent example is the tool launched by the University of Southern California Centre for Artificial Intelligence in Society called Protection Assistant for Wildlife Security (PAWS). This tool utilizes a game theory-based prediction model in order to anticipate and predict potential locations where poaching is highly likely to take place and also goes ahead and suggests various smart strategies for patrolling (USC CAIS, 2019). Similarly, Microsoft’s AI for Earth Program has led to the development of Systematic Poacher Detector (SPOT) tool which uses artificial intelligence to spot and provide real time detection through drone imagery of poachers, leading to better enforcement against them (Marsman, 2018). Nocturnal drone imagery is used for night surveillance and this technology has been successfully deployed in natural parks in Botswana (Marsman, 2018). Microsoft’s AI for Earth Program also includes various other successful deployment of artificial intelligence technologies. These include Project Premonition, being conducted in partnership with the University of Pittsburgh and the John Hopkins University and collaborations with iNaturalist and eBirds (Marsman, 2018). Project Premonition uses artificial intelligence to estimate and assess the relative abundance of insect species by collecting wing beat frequency data (Marsman, 2018) whereas Microsoft’s collaboration with iNaturalist and eBirds is aimed at using artificial intelligence for collection of data on wildlife and aquatic species which is pertinent for keeping track in their population, migration patterns and determining favorable ecosystems (Muraleedharan, 2018). Few other artificial intelligence tools and applications used in biodiversity conservation, monitoring and enforcement include TrailGuard AI, Wild Me and FieldKit (Snow, 2019).

Soil and Forest Resources Conservation. An agricultural technology startup called PEAT has developed an application known as Plantix which uses artificial intelligence technology to identify the relevant and potential nutrient deficiencies and other related defects in soil (Muraleedharan, 2018). Use of artificial intelligence has also been conducted for conservation of forests. 20tree.ai is a company using such technologies for forest intelligence through real time tracking, inventory management and suggesting sustainable conservation techniques (Moltzau, 2019). This is done through collection of data from satellite imagery which helps in forest composition data, forest insights, health of forests and prevailing threats such as droughts, deforestation, storm damage, insect plagues or other disturbances (Moltzau, 2019). Similarly, another company called aiTree Ltd. has developed a Forest Simulation Optimization System (FSOS) using a combination of

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artificial intelligence, cloud computing and big data technologies which monitors wildlife habitats, water resources and its quality, biodiversity, timber production, carbon storage and possible economic contributions by forests enabling better management and optimum utilization of forest resources (Moltzau, 2019). This system has been successfully implemented in Canada, British Columbia and China (Moltzau, 2019). Microsoft’s AI for Earth Program has also developed Terrafuse which uses artificial intelligence technology to link forest resources and climate related risks by collecting and predicting wild forest fires (Moltzau, 2019). Illegal deforestation and logging have been a serious problem in forests and a non- profit organisation called Rainforest Connection has come forward with a solution. This organisation has developed an acoustic monitoring system which helps in battling illegal deforestation in real time (Kesari, 2019). Recycled cell phones are left all over the forest and the system is trained to hear chainsaw sounds and in case it picks up such sounds, then it notifies forest rangers (Kesari, 2019). Few other artificially intelligent enabled technologies made for sustainable management of forest resources, conservation, monitoring and enforcement include convolutional neural networks being used by a data science company called Gramener, Global Forest Watch’s online platform, African Conservation Foundation’s use of big data and analytics-driven software known as InfoSphere Stream developed by IBM (Kesari, 2019). Moreover, the GainForest platform has also gone ahead and explored an innovative way of conserving forests through the combination of artificial intelligence and blockchain where interested persons can become stakeholders through blockchain powered smart contracts to join the battle for forest conservation (Skrabania, 2020).

Polar Ice Caps Conservation. The National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) have been two prominent players in using artificial intelligence space technologies for the conservation and monitoring of the quickly melting polar ice caps (United Nations, 2020). Two prominent examples of this initiative include the launch of the Ice, Cloud and Land Elevation Satellite (ICESat) and its successor ICESat-2 (United Nations, 2020). These satellites have been entrusted with the task of landscape mapping and analyzing data which will help in determining ice cover changes in Greenland and Antarctica with the use of Advanced Topographic Laser Altimeter System (ATLAS) (United Nations, 2020). Another prominent initiative for the conservation of polar ice caps has been taken by a polar scientist and explorer under the project called AI at the Ends of the Earth which is aimed at mapping and collecting data from Earth’s cryosphere through the use of advanced algorithms and drones (The Guardian Labs, 2019).

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Waste Disposal, Environment Risk and Impact Assessment. Effective waste management has been missing in most parts of the world, raising severe environmental concerns. The dilemma faced is not only limited to general waste types, but to also hazardous electronic and chemical wastes. However, artificial intelligence has successfully been used in waste management. In the year 2011, a Finnish company called ZenRobotics had developed a robotic waste sorter which used computer vision and machine learning to conduct its activities (Joshi, 2018). SFU Mechatronics Systems Engineering has developed an artificial intelligence powered smart equipment for recycling waste which uses a wide range of sensors to segregate waste to be dumped or to be recycled (Joshi, 2018). Not only the private sector, but the city authorities of Songdo, a city in South Korea have also been using Radio Frequency Identification (RFID) tags to bucket and segregate trash (Joshi, 2018). A Canadian company known as Intuitive has made an artificially intelligent tool called Oscar which aims to achieve zero waste in the area where it is deployed (How, 2019). Its achievements so far have been that it has diverted 10 tonnes of waste from landfills by recycling such waste, reduced 29 metric tonnes of carbon dioxide emissions and is capable of reducing carbon emissions to a level equivalent to removal of 2 fuel driven cars per year (How, 2019). The first artificially intelligent chatbot in the waste industry has been launched by BioHiTech known as BioHiTech Alto (Deoras, 2017). Few other artificially intelligent enabled waste segregation, handling and recycling technologies include the robot Clarke, developed by Carton Council in collaboration with AMP Robotics and Alpine Waste and Recycling, Bin-e intelligent waste management system, Max-AI developed by National Recovery Technologies and Ro- Boat, developed by an Indian company known as Omnipresent Robot Tech (Deoras, 2017). Hazardous waste management of electronic and chemical wastes have posed various dilemmas in environmental conservation efforts. But constant development of technology has been witnessed in this area as well. The Danish Technological Institute has partnered with a Swedish company called Refind Technologies to create a robot which will be crucial in the distinguishing and segregation of batteries in hazardous electric wastes (Nielsen, 2020). A company called Insilica has developed a large neural network to breakdown and assign functional characteristics to each and every component of the chemical being studied by data fed into it by the PubChem database (National Academies Press, 2019). This can help in better management of chemical waste to reduce its ill- effects on the environment. Recent years have also witnessed the use of artificial intelligence in Environmental Impact Assessments (EIA). Few examples of such use include MEXES, a rule based expert system which uses

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an antecedent- consequent reasoning model for environmental impact assessment and GAIA based upon a similar system and function such as that of MEXES (Cortes, 2000). More modern artificial intelligence tools for EIA include SCREENER which has successfully been deployed in Canada, Calyx formed by the Calyx Group of Products, ORBI developed by the Universidade Nove De Lisboa, IMPACT formed by the United States Department of Energy and EIAMAN (Khan and Panikkar, 2007). Environmental Compliance. The need for environmental compliance has arisen in the past few decades due to the increasing laws and regulations on international as well as national levels. Environmental compliance is often seen as a complicated and time-consuming process. However, the use of artificial intelligence has transformed the environmental compliance sector to a great extent. One prominent example of artificial intelligence deployment in the sector is by the company ehsAI. The tools developed by ehsAI function autonomously and scan permits and regulations of the company, enlists action items, shows discrepancies in company activities which might attract non- compliance penalties, reduces corporate environmental risk, reduces costs and increases the possibilities of corporate sustainability (ehsAI, 2020). Similarly, Enviance Management System is a cloud based artificial intelligence software which automates tracking and compliance of permit and license obligations (Enviance, 2019). Green Infrastructure, Green Jobs and Green Commercial Activities In present times, the need for green and energy efficient buildings, green spaces and urban greening has increased due to the severe threats faced by the global environment and unplanned urban areas. Artificial intelligence has been successfully used to facilitate such efforts. Green City Watch has implemented its artificial intelligence initiative in Indonesian cities which allows it to collect information about green versus paved area ratio, number of trees and infiltration capacity of parks in cities (Malliaraki, 2020). Similarly, an organisation called TreeMania mounted sensors in almost 5,500 trees in cities of Netherlands which used artificial intelligence to collect and provide real time data on moisture present in soil and as well as sent automatic email updates to the relevant local authorities, resulting in reduced tree loss (Malliaraki, 2020). Under its Urbana Champaign initiative, the University of Illinois has developed a tool capable of collecting data from social media, satellite imagery and computer vision to keep a track of urban green stormwater infrastructure (Malliaraki, 2020). One of the most prominent green and sustainable buildings in the world utilising artificial intelligence is the headquarters of Banco Bilbao Vizcaya Argentaria, a Spanish multinational financial services company (BBVA,

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2019). The artificial intelligence tools utilized in the building are tasked with energy management and distribution which has resulted in annual energy savings upto 5,766,731 kilowatt hours (kWh), 1,430 metric tonnes worth reduction of carbon dioxide emissions per year, greater use of natural light and optimum efficiency in climate control and air quality within the premises (BBVA, 2019). Artificial intelligence is also being used for converting all commercial activities into green, clean and sustainable activities and also in creating new commercial activities which are centered around it. This has also helped in creating new green jobs for the global population. One of the perfect examples is the partnership between Google and DeepMind who have developed a machine learning tool for cooling down Google’s servers leading to reduced energy consumption upto 40% (Sanu, 2019). Winnow has developed Winnow Vision, an artificially intelligent entity using computer vision that allows kitchens of hospitality businesses such as hotels and restaurants to drastically reduce food wastage and cut costs (Sanu, 2019). Few other commercial activities turning green due to the application of artificial intelligence include business activities of Walmart, Patagonia and Microsoft (Ilchenko, 2020). Climate Change, Conservation of Oceans and Other Water Resources Artificial intelligence has played a significant role in the efforts for battling climate change. This is evident through various technologies developed over the years. FRAME is an artificially intelligent enabled knowledge-based tool which studies extensive data and recommends the correct air pollution model (Cortes, 2000). Indian companies such as Blue Sky Analytics have developed various artificially intelligent enabled solutions such as Zorro, BreeZorro and Zuri which analyses colossal volumes of satellite data, ground level sensor measurements and public datasets to provide real time statuses on industrial emissions, air qualities, farm fires and the mapped environment (Ahaskar, 2020). Furthermore, JJAIBOT is an emotion based and interactive artificial intelligence robot which strives to educate and create awareness about climate change using a wide variety of datasets obtained from conservation projects and research (Jeffrey, 2019). A collaborative project between DHL and IBM has used artificial intelligence to reduce the total amount of emission output generated by DHL’s global logistics and transportation operations (Jeffrey, 2019). A startup company called Jupiter has developed an artificial intelligence system with the goal of making accurate predictions about climate change by feeding the system data from millions of ground-based sensors and orbital sensors (Davis, 2019). Another company called BioCarbon Engineering has developed artificially intelligent drones to 3D map suitable areas so that

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germinated seeds, necessary soil nutrients and moisture gels can be dropped, resulting in the widespread growth of trees to curb carbon emissions and as well as monitor the entire germination and health of such trees using such drones (Jeffrey, 2019). IBM’s Green Horizon Project uses artificial intelligence to predict pollution levels by analyzing environmental data and suggests pollution reducing tactics (Jeffrey, 2019). CYCLEGANS is a self- learning artificial intelligence tool developed by Cornell University which studies huge amounts of image data to analyse the before and after effects of extreme weather effects to determine the amount of climate change in a particular area (Farmen, 2019). Few other battling- climate change related artificial intelligence technologies include Airlitix (Farmen, 2019) and the Hangzhou City Brain Project (Greenman, 2019). Deployment of artificial intelligence technologies has not only been limited to climate conservation efforts but has also extended over to related activities such as cloud seeding. The Greater Saint John Cloud Seeding Program is one of the most highlighted examples. The program employs artificial intelligence and machine learning methods to control drones with climactic sensors to launch micro rockets filled with silver iodide for cloud seeding (ACAP Saint John, 2018). Utilization of artificial intelligence for the conservation of oceans and water resources has been substantial and has existed since early 2000s. This is apparent from the use of Operations Assistant and Simulated Intelligent System, developed by the South Florida Water Management District in the United States for smart management of more than 200 water structures extending along 3,200 kilometers of primary channels having a region of approximately 46,000 kilometers (Cortes, 2000). Recently, the Ocean Agency has created 50 Reefs, an advanced imaging artificial intelligence technology aimed at gathering and analyzing image data of almost all forms of coral reef present in oceans, so that the correct conservation decisions can be made in their respect (Jeffrey, 2019). A company known as Data 360 has used artificial intelligence for ocean floor mapping whereas the company Sinay has been using a combination of internet of things and artificial intelligence technologies to measure quality of water, weather data, waves data, ocean acoustics and locations of ships (Ponce De Leon, 2019). Similarly, a French initiative from IMT Atlantique has combined artificial intelligence with satellite remote sensing data to study the dynamics of ocean atmosphere (Microsoft Reporter, 2019). The deployment of artificial intelligence technologies is also being seen in responding to oil spills. The company Sea Machines has developed SM400, an autonomous vessel technology driven by artificial intelligence aimed to reduce chances of oil spills and in case of any collision leading to oil spills, in aiding the teams to actively respond to the scenario with real time data (Sea Machines,

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2020). The company KOSEQ has developed Rigid Sweeping Arms which are a set of artificially intelligent robotic arms and autonomous oil skimmers which aid in recovery processes after oil spills (Sea Machines, 2018). Other artificial intelligence technologies built on similar lines with the same functions include technologies by Microsoft, Emerson, Aker Solutions and GE (Dubrova, 2020). Natural Disaster Management Artificial intelligence technologies have been used for predicting, monitoring and responding to natural disasters such as earthquakes, landslides, volcanic eruptions, floods, tsunamis, hurricanes, cyclones, droughts, tornadoes, snowstorms, hailstorms and avalanches. The relevant technologies have been discussed below.

Earthquakes, Landslides and Volcanic Eruptions.

MIT has utilized a combination of artificial neural networks and convolutional neural networks to extrapolate frequency signals from below the Earth to understand its composition in a better manner and to predict earthquakes more effectively (Chu, 2020). The Karlsruhe Institute of Technology has developed a neural network to monitor and track seismic data which is capable of locating the epicenter of the earthquake (Karlsruhe Institute of Technology, 2019). Google in partnership with Harvard University are in the process of developing an artificial intelligence system capable of predicting the aftershock of earthquakes (Joshi, 2019). Few other artificial intelligence technologies for dealing with earthquakes include the technologies created by Los Alamos Laboratories, Caltech University and Stanford University (Fuller and Metz, 2018). Advancements in artificial intelligence technologies for detection of landslides include the Metro21: Smart Cities Initiative by Carnegie Mellon University (Kanowitz, 2020) and the various artificial technologies deployed by Governments across the world such as light detection ranging systems (LiDar), digital terrain models (DTM), interferometric synthetic aperture radar (InSar), machine learning, artificial neural networks, deep learning, convolutional neural networks, support vector machines and kernel logistic regression which rely on remote sensing data (Fawal, 2019). The Technical University of Berlin and the GFZ German Research Centre for Geosciences have collaborated and developed the Monitoring Unrest from Space (MOUNTS) platform which employs artificial intelligence to analyse satellite and remote sensing data to monitor volcanoes and volcanic activities on Earth (ScienceDaily, 2019). It is currently being successfully used for monitoring 17 volcanoes globally (ScienceDaily, 2019). Similarly, Multi-GAS is

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an artificial intelligence system which uses drones and a multicomponent gas analyzing system to measure the levels of key gases of active volcanoes by flying over the active craters (UN-SPIDER, 2020). NASA had also launched the Earth Observing One (EO-1) satellite which utilized artificial intelligence to monitor volcanic activity (Davies, 2015).

Floods, Tsunamis, Hurricanes and Cyclones.

STORMCAST is an artificial intelligence application for predicting severe hurricane storms and has been successfully implemented in the Scandinavian Peninsula (Cortes, 2000). IBM’s GRAF uses artificial intelligence to collect reliable weather-related information to predict thunderstorms, monsoon and flood patterns in advance and give warnings in advance to Governments so that they may prepare for such disasters (IBM, 2020). IBM’s DeepThunder Program also operates on similar lines (IBM100, 2020). The Indonesian Government has often utilized the k-NN and Naive Bayes algorithms to predict floods and the system’s accurate prediction rate is as high as 93.4% (Wahyono, 2019). Another artificial intelligence system called CENTAUR has been successfully used in urban areas to reduce the risk of urban flooding and is majorly installed in sewer systems and areas which are prone to flooding, operates with a multitude of smart sensors (University of Sheffield, 2020). Similar such technologies include Flood AI developed by UIHI Lab (UIHI Lab, 2020) and Google’s Flood Forecasting Initiative in India (Ganjoo, 2019). Artificial intelligence has also been successfully implemented in the monitoring of tsunamis, hurricanes and cyclones. Currently, Tokio Marine & Nichido Risk Consulting Co. has partnered with the National Research Institute for Earth Science and Disaster Resilience in Japan to develop an artificially intelligent tsunami prediction system (Osumi, 2019). Moreover, Liquid Robotics has invented a Wave Glider Robot fit with a microphone, hydrophone, satellite uplink, time lapse camera and artificial intelligence to monitor tsunamis by sailing in the oceans and seas (Reilly, 2017). Artificial intelligence technologies developed by Fujitsu Limited are aimed towards successful evacuation after tsunamis (Fujitsu, 2019). AccuWeather and other researchers from the United States have developed a system blended with artificial intelligence and machine learning to use remote sensing data to predict cyclones and hurricanes in the United States (Mohapatra, 2019). Previously, NASA and Development Seed had successfully used the NASA Cumulus Framework to predict the intricacies of Hurricane Florence in the United States with the use of machine learning (Deoras, 2018).

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Droughts and Tornadoes. Examples of artificial intelligence technologies for drought tracking and prevention include Microsoft’s AI for Earth Initiative which combines cloud technology to deliver results (Dedezade, 2019), initiatives by Vassar Labs (Economic Times, 2019) and the Rural Intelligence Platform by Digital Agricultural Services built for Australian farmers (Tyers, 2020). For tornado warnings, artificial intelligence systems such as IBM Watson, IBM Deep Thunder, initiatives by The Weather Company, Google and Panasonic have proved to be successful (Padma, 2020). A real estate data company from the United States called CoreLogic has developed Tornado Path Maps which uses United States National Weather Service radar data to track tornadoes and estimate the damage caused by it in different areas (Dol, 2020). The National Severe Storms Laboratory has also formed similar artificial intelligence technology which studies tornadic vortex signatures to determine results (NSSL, 2020).

Snowstorms, Hailstorms and Avalanches. Artificial intelligence technologies have not only been deployed for tracking and predicting snow storms, blizzards, avalanches and hailstorms, but have also assisted in snow removal activities. City administrations in New York have developed PlowNYC which uses a combination of internet of things, GPS and artificial intelligence to show each resident the date and time as to when city authorities will be plowing snow in the city areas (Bassulto, 2015). Furthermore, cities such as Buffalo, Boston and Minneapolis in the United States have also employed similar technologies (Bassulto, 2015). Autonomous vehicles such as THUNDAR and SNOWMENATOR have also contributed to the effort (Bassulto, 2015). Researchers at the National Center for Atmospheric Research in the United States have been using artificially intelligent facial recognition systems to predict hailstorms (Cappucci, 2019).

Multi-Disciplinary Analysis This section has been dedicated towards analyzing the various technologies at play for artificial intelligence hardware and software being utilized for enhancement in the global environment and its convergence with international environmental law.

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Name of Developer Artificial Intelligence Technology or Project Protection Assistant for Wildlife Security (PAWS)

Artificial Supporting Intelligence Hardware Software Type and Assisting Systems

University of Machine Southern Learning California Center for Artificial Intelligence in Society

Systematic Microsoft AI Image Poacher Detector for Earth Recognition, (SPOT) Deep Learning and Convolutional Neural Network

Cameras, Satellite

Purpose

Biodiversity Conservatio n

Drones, Biodiversity Sensors and Conservatio InfraRed n Cameras

Project Premonition

Microsoft AI for Earth, John Hopkins University and University of Pittsburgh

Convolutional Smart Neural Mosquito Network, Traps Deep Learning and Machine Learning

Biodiversity Conservatio n

Unnamed Project

Microsoft AI for Earth, iNaturalist and eBirds

Artificial and Motion Convolutional Triggered Neural Cameras Network

Biodiversity Conservatio n

TrailGuard AI

RESOLVE

Image Cameras Detection and Object Recognition

Biodiversity Conservatio n

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Wild Me

Computer Vision

FieldKit

Conservify

Machine Learning, Internet Things

Plantix

PEAT

Deep Learning

20tree.ai

Image Satellite Recognition and Object Recognition

Forest Conservatio n

Rule Based -System, MultiAgent System, Big Data and Cloud Computing

Forest Conservatio n

Forest Simulation aiTree Ltd. Optimization System

Drones and Biodiversity Cameras Conservatio n Sensors and Biodiversity FieldKit Conservatio of hardware n --

Soil Conservatio n

Terrafuse

Microsoft AI Machine for Earth Learning

Satellite

Forest Conservatio n

Unnamed Project

Rainforest Connection

Acoustic Monitoring

Recycled Cell Forest Phones Conservatio n

Unnamed Project

Gramener

Convolutional Neural Network

Satellite

Forest Conservatio n

InfoSphere Stream

IBM

Machine Sensors Learning, Data Mining and Statistical Modeling

Forest Conservatio n

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GainForest Platform

David Dao

Machine -Learning and Blockchain

Ice, Cloud and Land Elevation Satellite (ICESat) and ICESat- 2

National Aeronautics and Space Administrati on (NASA) and European Space Agency (ESA)

Machine Satellite, Polar Ice Learning, Sensors and Cap Computer Antenna Conservatio Vision, n Advanced Topographic Laser Altimeter System (ATLAS), Big Data Analysis and Cloud Computing

Unnamed Robot

ZenRobotics

Machine Robotic Learning and Waste Sorter Computer Vision

Waste Management

Unnamed Project

SFU Mechatronic s System

Machine Smart Bins Learning and Computer Vision

Waste Management

Unnamed Project

Songdo City Radio Authorities Frequency Identification (RFID)

Oscar

Intuitive

Machine Sensors Learning and Computer Vision

Waste Management

BioHiTech Alto

BioHiTech

Computer Vision, Natural Language

Waste Management

RFID Tags

Chatbot

Forest Conservatio n

Waste Management

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Processing and Machine Learning Ro-Boat

Omnipresen Machine t Robot Tech Learning and Computer Vision

Unnamed Project

Danish Technologic al Institute and Refind Technologie s

Deep Robot Learning and Computer Vision

Hazardous Waste Management

Unnamed Project

Insilica

Deep -Learning, Artificial Neural Network and Convolutional Neural Network

Hazardous Waste Management

MEXES

United States Rule Based -Government System

Environmen tal Impact Assessment

Calyx

Calyx Group Machine Satellite of Products Learning and Data Mapping

Environmen tal Impact Assessment

ORBI

Universidade Machine Satellite Nove De Learning and Lisboa Data Mapping

Environmen tal Impact Assessment

ehsAI

Environmen tal Compliance

Machine Learning

Cameras, Waste Unmanned Management Water Surface Vehicle

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Unnamed Project

Green Watch

Unnamed Project

TreeMania

Machine Sensors Learning and Internet of Things

Green Infrastructur e

Urbana Champaign

University of Computer Satellite Illinois Vision, Image and Object Recognition

Green Infrastructur e

Unnamed Project

Banco Bilbao Vizcaya Argentaria

Green Infrastructur e

Unnamed Project

Google and Machine DeepMind Learning

Winnow Vision

Winnow

Blue Analytics

City Machine Satellite Learning, Data Mapping and Synthetic Aperture Radar

Sky Zorro, BreeZorro and Zuri

Machine Building Learning and Internet of Things

Green Infrastructur e

Data Server Green Machines Commercial Activities and Green Jobs

Computer Camera Vision and Scale Machine Learning

and Green Commercial Activities and Green Hobs

Artificial Neural Network, Convolutional Neural Network, Machine

and Climate Conservatio n

Satellite Sensors

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Learning and Data Mapping JJAIBOT

Julian Jewel Jeyaraj

Machine Chatbot Learning, Natural Language Processing, Natural Language Generation and Natural Language Understandin g

Unnamed Project

DHL IBM

Unnamed Project

Jupiter

Machine Learning

Orbital Sensors

Climate Conservatio n

Unnamed Project

BioCarbon Engineering

Computer Drones Vision, Machine Learning and Data Mapping

Climate Conservatio n

Green Horizon IBM Project

Cognitive -Computing, Big Data Analysis and Internet of Things

Climate Conservatio n

CYCLEGANS

Machine Satellite Learning and Artificial Neural Network

Climate Conservatio n

and Machine Learning

Cornell University

Climate Conservatio n

Logistics Climate Vehicles and Conservatio Equipment n

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Airlitix

Airlitix Inc.

Hangzhou City Alibaba Brain Project

Machine Learning

Drones

Climate Conservatio n

Deep Cameras and Climate Learning, Big Sensors Conservatio Data n Processing, Image and Object Recognition

The Greater Saint ACAP Saint Machine John Cloud John Learning, Seeding Program Computer Vision and Deep Learning

Drones, Rockets, Climactic Sensors

Climate Conservatio n

Operations Assistant and Simulated Intelligent System

South Florida Water Management District

Rule Based -System

Ocean and Water Resource Conservatio n

50 Reefs

Ocean Agency

Image -Recognition, Object Recognition and Deep Learning

Ocean and Water Resource Conservatio n

Unnamed Project

Data 360

Machine -Learning and Data Mapping

Ocean and Water Resource Conservatio n

SM400

Sea Machines

Computer Ships Vision and Vessels Light Detection and

and Ocean and Water Resource

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Ranging System

Rigid Arms

Sweeping KOSEQ

Conservatio n

Computer Robotic Ocean and Vision, Light Arms, Ships Water Detection and and Vessels Resource Ranging Conservatio System and n Machine Learning

Unnamed Project

MIT

Artificial -Neural Network and Convolutional Neural Network

Earthquake Management

Unnamed Project

Karlsruhe Artificial -Institute of Neural Technology Network and Convolutional Neural Network

Earthquake Management

Unnamed Project

Google and Deep Harvard Learning University

Earthquake Management

Satellite

Landslide Management

Metro21: Smart Carnegie Cities Initiative Mellon University

Artificial Neural Network, Convolutional Neural Network, Machine Learning, Deep Learning, Light Detection

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Ranging System, Digital Terrain Model and Interferometri c Synthetic Aperture Radar Monitoring Technical Machine Satellite Unrest From University of Learning and Space (MOUNT) Berlin and Artificial GFZ Neural German Network Research Centre for Geosciences

Volcano Management

Multi-GAS

Researchers from Italy

Machine Learning, Deep Learning and Computer Vision

Drones, Volcano Cameras and Management MultiComponent Gas Analyser System

STORMCAST

StormCast Project

Distributed Artificial Intelligence

Satellite

Hurricane Management

GRAF

IBM

Machine Learning, Cloud Computing and Supercomputi ng

Supercomput ers

Flood, Thunderstor m and Monsoon Management

DeepThunder

IBM

Machine Learning, Cloud Computing

Supercomput ers

Flood and Weather Management

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and Supercomputi ng CENTAUR

University of Machine Sensors Sheffield Learning and Internet of Things

Flood Management

Flood AI

UIHI Lab

Natural -Language Processing, Natural Language Understandin g and Machine Learning

Flood Management

Flood Forecasting Initiative

Google

Machine -Learning and Data Mapping

Flood Management

Unnamed Project

Tokio Machine -Marine & Learning and Nichido Risk Data Mapping Consulting Co. and National Research Institute for Earth Science and Disaster Reliance

Tsunami Management

Wave Robot

Glider Kobe University

Computer Vision, Machine Learning and Deep Learning

Microphone, Tsunami and Hydrophone, Volcano Satellite, Management Cameras and Robot

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Unnamed Project

Fujitsu Limited

Deep Learning

Mobile Phone

Tsunami Management

Unnamed Project

AccuWeathe r

Machine Learning

Hurricane and Cyclone Management

NASA Cumulus Nasa and Machine Framework Developmen Learning t Seed

Hurricane and Cyclone Management

Unnamed Project

Vassar Labs

Machine Sensors, Drought Learning and Automatic Management Internet of Weather Things Station and Satellite

Rural Intelligence Digital Platform Agricultural Services

Machine Satellite Learning and Cloud Computing

Drought Management and Soil Conservatio n

Watson

IBM

Machine Learning, Cloud Computing and Supercomputi ng

Tornado Management

Tornado Maps

Path CoreLogic

PlowNYC

New York City Administrati on

Supercomput ers

Machine Satellite Learning and Data Mapping

Tornado Management

Machine Sensors Learning and Satellite Internet of Things

and Snow Management

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THUNDAR

North Computer Autonomous Snow Dakota State Vision, Snow Plowing Management University Machine Vehicle Learning and Deep Learning

SNOWMENAT OR

North Computer Autonomous Snow Dakota State Vision, Snow Plowing Management University Machine Vehicle Learning and Deep Learning

Unnamed Project

National Facial, Image -Center for and Object Atmospheric Recognition Research

Hailstorm Management

The table above shows that the artificial intelligence technologies used in the global environment include machine learning, deep learning, artificial and convolutional neural networks, computer vision and the like, often combined with supporting hardware and allied non- artificial intelligence technologies such as internet of things, big data analysis and cloud computing. The advancements in the sector have been significant, but artificial intelligence technologies do not exist for mineral resources conservation or for damage reversal caused to the environment after natural or manmade disasters. This is capable of raising severe concerns since damage repair, restoration and reversal is an indispensable facet of environmental sciences, especially at a time where irreversible damage to the environment has already taken place. Albeit the fact that wide and extensive application of these technologies have already been deployed in the global environment, the scheme of international environmental law has failed to support it adequately. Specialised agencies of the United Nations such as the United Nations Environment Programme, International Maritime Organisation, United Nations Framework Convention on Climate Change and World Meteorological Organisation have shown active support through artificial intelligence development projects in recent years (International Telecommunications Union, 2019). Moreover, the United Nations Interregional Crime and Justice Research Institute has also established

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a Centre for Artificial Intelligence and Robotics to focus expertise on artificial intelligence (Law Library of Congress, 2019). Despite this, multilateral initiatives in respect to artificial intelligence in the global environment are almost non- existent and only few General Assembly Resolutions exist in the sphere (Law Library of Congress, 2019). The Organisation of American States, European Union and the Council of Europe have formed few regional instruments, but they seem to be generic in nature and do not deal with specific artificial intelligence technologies in its application to the global environment.

Outcome Analysis This section is dedicated towards showcasing the deficiencies in the areas of international environmental governance with the help of existing unregulated advancements of artificial intelligence technologies. The illustration below shows the percentages of types of artificial intelligence technologies used in the global environment-

Fig. 2. Percentages of types of artificial intelligence technologies used in the global environment

Therefore, in light of all the chapter sections above, it is apparent that the global environment has deeply and most prominently benefitted from machine learning followed by the remaining technologies and that reliance on hardware in the global environment is wellbalanced. However, the chapter sections have also showcased that no specific regulations for environment specific artificial

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intelligence technology exists in the international sphere. Moreover, the fact that most developers are either private sector companies or research organisations and labs from Universities and that, Government affiliated developers are rare, the need for sufficient multilateral, bilateral and regional legal instruments for capacity building, investment incentivization, increased and cross- border development done keeping in mind relevant national standards and issues revolving around artificial intelligence technologies is currently the need of these times.

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International Energy Law

Manohar Samal

Introduction The energy sector is associated with the production and supply of “energy” obtained from various fossil fuels as well as renewable sources which is used to provide services to other industries, Governments or the general population (Chen, 2020). Few fossil fuel-based sources of energy include natural gas, petroleum products, oil and nuclear power whereas, renewable sources include biofuels, hydropower, wind and solar power (Chen, 2020). Artificial intelligence technology has been deployed in every sphere and this includes the global energy sector. It’s significance in evolving human interaction with energy resources has led to possibilities of exploration and implementation in new areas that will result in achieving development in the global energy sector in an environmentally sound, sustainable, safe and economical manner. In order for this to happen, it is undeniable that the correct approach in international law has to be employed so that the entire global society of nations are enabled to participate in the synthesis between technology and the global energy sector. Thus, in light of the above, this chapter has been dedicated towards discussing the close nexus between artificial intelligence, the global energy sector and international energy law which will show the evolution and advancements done in the field and also aid in highlighting its lacunae.

Legal Introduction Traces of international energy law can be found in both public as well as private international law. In public international law, the major portion of international energy law consists of conventions, treaties and other international instruments and the minor portion comprises certain overlapping and specific customary law principles. However, the sphere of private international law has led to the most amount of advancements in international energy law. This is mainly due to cross border investment contracts being signed between Governments and other Governments, Governments with corporations and companies with other companies in all possible permutations and combinations.

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Public International Law

Customary Law. Traditionally, the use of energy resources was solely within the ambit of sovereignty of States. But, the subsequent increase in globalization has shown the need of an international energy law (Bruce, 2014). The full-fledged progress in international energy law began after the 1970s (Wawryk, 2014). In terms of customary international law, a set of common practices have been followed in the global energy sector. These include the use of standardized model contracts made as per business customs, model farmout agreements, model unitization agreements, model confidentiality agreements, model concession contracts and other relevant business customs followed in the global energy sector (Wawryk, 2014). Moreover, over the span of time, defined principles have also originated. The customary law principles of international energy law are the principle of natural resource sovereignty, principle of access to modern energy services, principle of energy justice, principle of prudent, rational and sustainable use of natural resources, principle of the protection of the environment, human health and combating climate change, energy security and reliability principle and principle of resilience (Heffron, 2018). The principle of natural resource sovereignty revolves around the permanent and inviolable sovereignty of countries over the natural resources present in their own country (Heffron, 2018). The principle of access to modern energy services focuses on creating modern energy access for all communities in the world and also converges with the sphere of human rights (Heffron, 2018). The principle of energy justice originated in the late 20th and early 21st centuries and emphasizes upon distributional, procedural and recognitional justice for energy (Heffron, 2018). The principle of prudent, rational and sustainable use of natural resources combines with sustainable sciences and focuses on conservation, optimal management, rational use and elimination of unsustainable practices in the field of energy (Heffron, 2018). The principle of the protection of the environment, human health and combating climate change is true to its nomenclature and focuses on the interdependence and inter- relationship of environment, human health and climate change with energy resources (Heffron, 2018). The energy security and reliability principle emphasize on secure supply and demand which is incessant in nature and available at affordable prices (Heffron, 2018). The principle of resilience is focused upon strengthening the transformation of the global energy sector to maintain its resilience and also includes tackling various issues which can affect the global energy sector (Heffron, 2018).

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International Legal Instruments. There are various conventions, treaties and other international instruments in the bilateral, multilateral as well as the regional sphere which have been formulated in the furtherance of international energy law. These include the Sustainable Energy for All (SE4ALL) Initiative, United Nations Framework Convention on Climate Change 1992, Kyoto Protocol 1997, Paris Agreement 2015, Energy Charter Treaty 1998, Stockholm Declaration 1972 and the Rio Declaration 1992 (Bruce, 2013). The international instruments are limited in nature mainly due to the fact that the global energy sector has quickly progressed due to private international law and not public international law. Private International Law

Since all countries extend permanent and complete sovereignty over their own natural resources, contracts and licenses between Governments and other Governments, companies and Governments and companies with other companies have played a pivotal role in its exploitation. This is where the sphere of private international law comes in since domestic laws and regional practices dominate the relations between stakeholders in the global energy sector. This includes a wide range of aspects and issues such as choice of law, applicable jurisdiction, nations empowered to enforce, applicable judicial forums including domestic, regional and international judicial forums. Few landmark international arbitration awards in the global energy sector are the Yukos Universal arbitration, Stabil LLC case and the Burlington Resources arbitration (Alford, 2018). The complex nature of contracts and licenses in this sphere, involvement and dispensation of huge wealth in this sector has enabled companies and few Governments to overexploit the natural resources of few nations for the mere purpose of gaining profits, leading to increased unsustainable practices and environmentally degrading strategies.

Conjunction with Artificial Intelligence The role of artificial intelligence in advancing the global energy sector has been humongous. This is mainly because its contribution has been seen the most in making energy cheaper and more reliable (Makala and Bakovic, 2020). Artificial intelligence has also eliminated the amount of wastage and kept supply in equilibrium with the demand for energy (Makala and Bakovic, 2020). Various advancements have been seen in grid management and monitoring, sector coupling, energy trading platforms, energy efficiency and consumption management, obtaining energy from hazardous sources in a safe and

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environment friendly manner and in increasing the amount of clean and sustainable energy sources due to the use of artificial intelligence.

Case Studies This section of the chapter is dedicated towards analyzing case studies of artificial intelligence applications in wide areas of the global energy sector including smart grids, sector coupling, grid monitoring, energy trading, virtual power plants, power consumption, energy efficiency, energy management, obtaining energy from hazardous sources safely and clean and sustainable energy sources which will help in deriving the intended results. Smart Grids and Grid Monitoring

Artificially intelligent enabled technologies have led to the development of smart grids and improved grid monitoring in the energy sector. Veritone Energy is one example which has developed an artificial intelligence enabled tool to predict energy supply, price and demand leading to better grid management (Jones, 2020). The company Lition is another example who uses an artificially intelligent energy exchange to improve the power plant revenue as well as reduce costs for customers by better energy efficiency and management (Energy Startups, 2020). Other companies which use similar technologies include AutoGrid Systems and Origami Energy (Energy Startups, 2020). Similarly, National Grid Partners and SparkCognition have collaborated to use artificial intelligence to automate predictive maintenance, customer security and cybersecurity monitoring in respect of these automated tasks (Horwitz, 2020). Another stellar example in smart grid management is the LVCloud technology used by the company, EA Technology, which uses artificial intelligence for smart grid management by aggregating and interpreting data received from time- domain reflectometers (TDR) and ALVIN Transformer Load Monitors (ATLM) (Sulikowski, 2019). The United States of America Energy Department’s SLAC National Accelerator Laboratory has launched the Visualisation and Analytics of Distribution Systems with Deep Penetration of Distributed Energy Resources (VADER) which uses artificial intelligence for real time optimisation, automation of distributed planning and for making operational decisions for energy utilities (Frye, 2018). Similarly, the company Nnergix combines machine learning and cloud computing to ensure better smart grid management and monitoring for renewable energy (Frye, 2018). The Argonne National Laboratory has developed an artificial intelligence technology for better grid

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monitoring and smart grids management (Kim, 2019). Energy Trading and Virtual Power Plants The area of energy trading and virtual power plants has also seen significant advancements due to the use of artificial intelligence. The company Stem has developed Athena which uses machine learning and predictive analytics to maximize value of energy storage with the help of a virtual power plant (Frye, 2018). Fuergy is another company which uses artificial intelligence for energy trading activities under the name and style of “brAin”. The algorithm buys electricity from the energy market when the energy rates are comparatively lower and stores it in its battery so that it can use it at a later stage when the prices are relatively higher, resulting in reduced costs for the ultimate customer (Rabbitte, 2019). Fuergy’s artificially intelligent technology is also capable of selling unused renewable energy back to the energy market automatically depending upon the usage patterns of the customer (Rabbitte, 2019). Axpo’s Big Data Project uses machine learning for efficient energy trading (Axpo, 2019). Other companies which use artificial intelligence for energy trading include DTE, Exelon and Vistra (Kram, 2019). Toshiba’s Virtual Power Plant Solution uses artificial intelligence and the internet of things to launch its own virtual power plant which bundles together scattered energy sources in a network and permits power companies to effectively handle the generation, transmission and distribution of electric power (McMahan, 2019). Other companies deploying artificial intelligence technologies in their virtual power plants include Enbala Power Networks, Sunverge Energy, Siemens, Advanced Microgrid, Green Charge and Sonnen (Clancy, 2017). Power Consumption, Energy Efficiency and Management The contribution of artificial intelligence has been significant in improving energy efficiency, management and consumption. The DeepMind AI developed by collaboration between Google and IBM uses machine learning, artificial neural networks and deep neural networks to reduce the total amount of energy and cooling costs of Google by efficient power consumption and management (Evans and Gao, 2016). Microsoft’s Project Natick is currently conducting full-fledged research in order to determine the favourability of subsea data centers with the help of renewable energy and using artificial intelligence (Microsoft, 2020). It is assessed by the team working on the project that this would not only help in reducing the overall power consumption, but

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lead to better energy efficiency (Microsoft, 2020). A startup named Innowatts uses artificial intelligence for better energy management and monitoring through an automated toolkit developed by them (Kimani, 2020). Another startup company called VIA uses artificial intelligence and blockchain to predict the risks which might be faced due to faulty or deteriorating power transmission gear, leading to better safety and management possibilities (Kimani, 2020). Similarly, a Korean company called Alchera uses artificial intelligence enabled image and object recognition to monitor power lines and power sub- stations in real time which helps in increasing the safety and management capabilities of the company (Kimani, 2020). IBM and the United States Department of Energy have collaborated to introduce the SunShot Initiative which uses artificial intelligence to predict the production capacities and output of power stations resulting in better management and efficient generation of energy (Sozontov, 2019). Deep Mind Technologies Ltd. uses neural networks to reduce the overall energy usage and also, simultaneously reduce their energy costs (Sozontov, 2019). Other companies which have used artificial intelligence technologies to reduce their energy usage and ensure better energy efficiency include General Motors, Verdigris Technologies, National Grid UK, Power Scout, Schleswig- Holstein Netz AG and Upside Energy (Sozontov, 2019). Obtaining Energy from Hazardous Sources Safely Use of artificial intelligence has been seen to a paramount extent in the subsphere of safe obtaining of energy from sources which are hazardous such as the mining, oil and gas and nuclear industry. These have been discussed below. Mining. Dassault Systemes is a software company which has developed an artificial intelligence system called the 3D Experience Platform (Bese, 2018). This platform enables natural resource and mining companies to connect and collect all forms of relevant data such as geology, engineering, production and market data so that the relevant experts and stakeholders in the company can effectively indulge in business planning, development and operations through centralized data. The platform also enables the relevant experts and stakeholders from the companies to modify, breakdown, adjust or re-optimize the suggestions given by the system (Bese, 2018). Various companies have also started relying upon autonomous technology for field related mining activities. One stellar example is the company, Komatsu Mining that has built multifarious artificial intelligence powered autonomous equipment (Schmelzer, 2019). These autonomous equipment are dispatched in hostile environments, where it is dangerous for human beings to conduct any form of activities and

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the equipment comprises a wide range, right from autonomous excavators and autonomous transportation equipment to autonomous loading vehicles (Schmelzer, 2019). The success of such artificial intelligence hardware is dependent upon an equally responsive artificial intelligence software, referred to as “digital mines” in common parlance. These digital mines are responsible for connection, coordination, responsiveness and decentralization of control of the intelligent hardware (Schmelzer, 2019). Goldspot Discoveries Corp has been employing artificial intelligence to improve its gold exploration and mining activities by using advanced analytics and simulation modeling created by the technology (Karatzoglou, 2020). Rio Tinto uses an autonomous haul truck fleet for its iron ore mining activities (Karatzoglou, 2020). Another company called ThoroughTecSimulation has developed wearable sensors under their CyberMine range of products, for mining workers which automatically collect information about worker behaviour, worker safety and security which helps companies in determining the needs of their miners (Karatzoglou, 2020). RockMass Technologies has deployed artificial intelligence to study rock surfaces of quarries and mining zones to aid planning process and for swift decision making (Deloitte, 2020). Ionic Engineering uses machine learning and image recognition to reduce the impact of error by its employees (Deloitte, 2020). The company Wipware uses artificial and convolutional neural networks to improve management of employees, increasing equipment life and improves the complete mining processes (Deloitte, 2020). Freeport- McMoRan has built a customized artificially intelligent entity to enhance its copper mining activities and its testing has showcased that the technology is capable of increasing the output, reducing wastage and increasing efficiency of its activities (Conger, 2020). Nia is an artificial intelligence platform developed by Infosys which uses machine learning and cognitive computing to enhance digital control systems and give outputs in the fields of geology, topography, geo- mechanics, engineering and mineralogy with the help of aerial photography, satellite imagery and 3D Maps to improve mining activities (Infosys, 2020). Oil and Gas. Artificial intelligence has been successfully deployed in the oil and gas industry as well. ExxonMobil has partnered with IBM to use artificial intelligence in collaboration with quantum computing that helps in developing realistic simulations leading to better planning and calculations (Kimani, 2020). The company BP Plc deploys artificial intelligence for similar purposes (Kimani, 2020). The Royal Dutch Shell company uses a combination of deep learning, machine learning, computer vision and a wide variety of autonomous hardware for oil drilling which helps in increasing the safety of its employees

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and reduction of accidents and spills (Kimani, 2020). The company Total has deployed autonomous robots to carry out oil and gas drilling activities and similarly, Gazprom and Yandex have collaborated to use machine learning for drilling, well completion, modeling strategies of oil- refining and optimizing other processes related to technology (Sennaar, 2019). Microsoft has collaborated with Baker Hughes and C3.ai to use artificial intelligence technologies for inventory management, energy management, equipment reliability and predictive maintenance in the oil and gas activities of Baker Hughes (Nair, 2020). Nuclear Power. The use of artificial intelligence for nuclear power can be traced back to as far as 1983 when the Electric Power Research Institute of the United States of America introduced Reactor Emergency Alarm Level Monitor (REALM) which was a rule based expert system following the antecedent- consequent model for raising an alarm during emergencies and informing the level of emergency in nuclear power plants (Uhrig, 1989). The United States Department of Energy’s Advanced Research Projects Agency Energy Division has awarded grants to GE Research, MIT, Argonne National Laboratory and the University of Michigan to conduct studies on how artificial intelligence can positively impact the nuclear power sector by reducing costs, transforming reactors and ensuring high and healthy levels of competitiveness between nuclear power plants (Strauss, 2020). This will be done with the help of the Humble AI software under the Generating Electricity Managed by Intelligent Nuclear Assets (GEMINA) program (Strauss, 2020). The Electric Power Research Institute has also developed Virtual Environment For Reactor Applications (VERA) which uses artificial intelligence to predict the behaviour of nuclear reactor’s core (Yurman, 2020). The Korean Electric Power Company uses neural networks to monitor thermal margins in nuclear power plants (Uhrig and Hines, 2000). Lancaster University is developing semi- autonomous robots in order to increase the safety and possibilities of post- nuclear accident cleanups (Zaremba, 2019). Framatome has collaborated with ADAGOS to improve activities and functioning in the nuclear power sector with the help of the NeurEco Architecture (Framatome, 2020).

Clean and Sustainable Energy Sources. Artificial intelligence has been successfully utilized in the subsphere of generation and management of clean and sustainable energy sources such as wind power, solar power, hydropower and biofuels. The technologies making this possible have been specified below.

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Wind Power.

A significant number of advancements in the field of artificial intelligence has been seen when it comes to generation and management of wind power. Tilt Renewables has made its own artificially intelligent wind farm (Nhede, 2019). Google has been deploying neural networks and machine learning to assess the productivity of wind farms and also by assessing the weather conditions which help in better decision making in wind farms (McMahon, 2020). The SmartWind Project by Zorlu Enerji uses artificial intelligence to maintain coordination between individual wind turbines and equipment health to improve management at wind farms (Cornelissen, 2020). Vestas Wind Systems in collaboration with Utopus Insights Inc. uses artificial intelligence for boosting wind power generation (Bloomberg, 2018). The most peculiar usage of artificial intelligence has been done by BirdVision, a German company which uses artificial intelligence to stop the blades of wind turbines when birds come near it and restarts it once all birds are at a safe distance from the wind turbine blades (Jungblut, 2020). This helps in maximising bird safety along with co- existence of the wind turbines. Solar Power.

The Australian Energy Market Operator uses a system enabled with selfforecasting artificial intelligence technology which helps them in forecasting the amount of electricity generated by solar power plants resulting in maximising the amount of dispatch of solar energy in the grid and the elimination of frequency control service costs (Jouault, 2019). The company New Energy Solar uses artificial intelligence in the Manildra Solar Plant to improve its performance and management (Jouault, 2019). University of Auvergne has used artificial intelligence technologies to achieve greater levels of optimization in solar panels (Sagar, 2019). Companies like Avrio Energy, Energly and the Solar Labs are working for improving energy efficiency and increasing energy output from solar energy sources (Sharma, 2020). The companies Xcel and Nnergix use artificial intelligence for weather predictions so that accurate forecasts of solar energy which can be obtained from a solar farm can be determined (Sennaar, 2019). Similarly, the company OpenClimateFix also uses tools enabled by artificial intelligence to predict solar energy generation with the help of machine learning and neural networks (Gentry, 2020). Hydropower.

Agder Energy and the University of Agder have partnered with each other to develop artificial intelligence tools using deep learning and reinforcement

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learning which will help them in managing hydroelectric energy production with fully autonomous capabilities with extremely minimum human interference (Wehus, 2017). Skelleftea Kraft AB is also working towards developing an artificial intelligence tool which will help them in monitoring hydropower plants in a more efficient way with the use of machine learning and neural networks (Stark, 2019). Voith and Landsvirkjun have partnered together to develop an artificial intelligence technology for acoustic monitoring in Budarhals Hydropower Plant which will help in assessing machine damage and maintenance requirements in the power plant (Power Engineering International, 2018). Researchers from Cornell University are experimenting with artificial intelligence to introduce a tool which will help in reducing the overall greenhouse emissions in the hydropower obtained. This is mainly because although the process may be renewable and clean, the manner in which hydropower plants are made are not so environmentally friendly showing the requirement of such reduction (Cornell University, 2019). Inovia AI is a tech company which uses artificial intelligence in Saxnas Hydro Station in Sweden for surveillance, receiving data from equipment in real time and indulging in energy trade (Inovia AI, 2020). Another company called ABB has developed its own artificial intelligence enabled tools for integrating automation with functioning of the hydropower plant, hydro turbine control and predictive maintenance (ABB, 2020). Biofuels. Researchers from the Indian Institute of Technology Hyderabad have developed a tool powered by artificial intelligence which helps in determining the impediments and the factors related to incorporating biofuels to the fuel sector in India in order to reduce the amount of fossil fuel emissions (Singh, 2020). Researchers from the chemical engineering department of the Carnegie Mellon University have deployed machine learning to develop catalysts for the production of biofuels (Carnegie Mellon University, 2020). The company LanzaTech is utilising artificial intelligence technologies developed by TeslaGen to recycle waste in a manner which will help in creating biofuels (Biofuels International, 2018). A research group from the University of Cordoba in Spain has developed an artificially intelligent predictive model which will help in making accurate predictions about the biological behaviour of neurons (Sapp, 2018).

Multi-Disciplinary Analysis This section has been dedicated towards analyzing the various technologies at play for artificial intelligence hardware and software being utilized for

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enhancement in the global energy sector and its convergence with international energy laws. Name of Artificial Intelligen ce Technolo gy or Project

Developer

Artificia Supporti l ng Intelligen Hardware ce Software Type and Assisting Systems

3D Experienc e Platform

Dassault Systems

Machine Learning and Think Optimized Short Interval Control

Unnam ed Technolog y

Komatsu Mining

Pattern Matching, Predictive Analysis, Computer Vision and Internet of Things

Autonom ous Excavators, Autonomous Loading Vehicles, Autonomous Transportati on Vehicles and Sensors

Veritone Energy

Machine Learning and Internet of Things

Sensors

Smart Grids and Grid Monitorin g

Lition

Machine Learning and Blockchain

Smart Grids and Grid

Unnam ed Technolog y Unnam ed Technolog y

---

Purpos e

Mining

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Monitorin g AutoGr id DROMS, AutoGrid DERMS and AutoGrid ESMS Unnam ed Technolog y Unnam ed Technolog y

AutoGrid Systems

Machine Learning and Big Data Analysis

Smart Grids and Grid Monitorin g

Machine Learning and Big Data Analysis

Smart Grids and Grid Monitorin g

National Grid Machine Partners and Learning SparkCognition and Cognitive Computing

Smart Grids and Grid Monitorin g

Origami Energy

LVClou

EA Technology

Machine Learning and Internet of Things

Sensors

Smart Grids and Grid Monitorin g

Athena

Stem

Machine Learning and Predictive Analysis

Energy Trading and Virtual Power Plants

Visualis United States of Machine ation and America Energy Learning Analytics Department’s of SLAC National

Smart Grids and Grid

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Distributio Accelerator n Systems Laboratory with Deep Penetratio n of Distribute d Energy Resources (VADER)

Monitorin g

Unnam ed Technolog y

Nnergix

Machine Learning and Cloud Computing

---

Smart Grids and Grid Monitorin g

VoltaCh em Program

TNO

Artificial Neural Networks, Convolutio nal Neural Networks and Deep Learning

Smart Grids and Grid Monitorin g

Unnam ed Technolog y

Argonne National Laboratory

Machine Learning, Supervised and Unsupervis ed Learning

---

Energy Trading and Virtual Power Plants

brAin

Fuergy

Machine Learning and Blockchain

---

Energy Trading and Virtual Power Plants

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Unnam ed Technolog ies

DTE Energy, Machine Exelon and Vistra Learning, Big Data Analysis and Cloud Computing

Energy Trading and Virtual Power Plants

Big Data Project

Axpo

Machine Learning

Energy Trading and Virtual Power Plants

Toshiba Virtual Power Plant Solution

Toshiba

Artificial Neural Networks and Internet of Things

Sensors

Energy Trading and Virtual Power Plants

DeepMi nd AI

Google IBM

Machine Learning, Artificial Neural Networks and Deep Neural Networks

Power Consumpt ion, Energy Efficiency and Manageme nt

and

Project Natick

Microsoft

Machine Learning and Cloud Computing

Power Consumpt ion, Energy Efficiency and Manageme nt

Unnam

Innowatts

Machine Learning

Power Consumpt

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Technolog y

ion, Energy Efficiency and Manageme nt

Unnam ed Technolog y

VIA

Machine Learning and Blockchain

Unnam ed Technolog y

Alchera

Image Cameras Recognitio and Sensors n and Object Recognitio n

Power Consumpt ion, Energy Efficiency and Manageme nt

Machine Supercom Learning puters and Supercomp uting

Oil and Gas

Machine Learning

Oil and Gas

Unnam ed Technolog y Unnam ed Technolog y Unnam ed Technolog y

ExxonMobil and IBM

BP Plc

Royal Shell

Dutch

Power Consumpt ion, Energy Efficiency and Manageme nt

Machine Autonom Oil and Learning, ous Vehicles Gas Computer and Vision and Autonomous Robots

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Deep Learning SunShot IBM and the Artificial Initiative United States of Neural America Networks Department of and Energy Convolutio nal Neural Networks

Power Consumpt ion, Energy Efficiency and Manageme nt

Unnam ed Technolog y

Power Consumpt ion, Energy Efficiency and Manageme nt

Machine Learning

Power Consumpt ion, Energy Efficiency and Manageme nt

Machine Learning, Artificial Neural Networks and Internet of Things

Sensors

Power Consumpt ion, Energy Efficiency and Manageme nt

Unnam ed Technolog y

Deep Mind Artificial Technologies Ltd. Neural Networks, Deep Neural Networks and Convolutio nal Neural Networks General Motors

Verdigris Technologies

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Unnam ed Technolog y

National UK

Grid

Machine Learning and Computer Vision

Drones, Power Cameras and Consumpt Sensors ion, Energy Efficiency and Manageme nt

Unnam ed Technolog y

PowerScout

Machine Learning

Power Consumpt ion, Energy Efficiency and Manageme nt

Unnam ed Technolog y

SchleswigHolstein Netz AG

Machine Learning and Artificial Neural Networks

Power Consumpt ion, Energy Efficiency and Manageme nt Power Consumpt ion, Energy Efficiency and Manageme nt

Unnam ed Technolog y

Upside Energy

Machine Learning and Predictive Analysis

Unnam ed Technolog y

Goldspot Discoveries Corp

Machine Learning and Big Data Analysis

Mining

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Unnam ed Technolog y

Rio Tinto

Comput er Vision and Internet of Things

Autonom ous Haul Trucks, Cameras and Sensors

Mining

CyberM ine

ThoroughTecSi mulation

Artificial Wearable Neural Devices, Networks Cameras and and Sensors Internet of Things

Mining

Unnam ed Technolog y

RockMass Technologies

Machine Learning and Internet of Things

Sensors

Mining

Unnam ed Technolog y

Ionic Engineering

Machine Learning and Image Recognitio n

Cameras

Mining

Unnam ed Technolog y

Wipware

Machine Learning

Mining

Unnam

FreeportMcMoRan

Machine Learning, Deep Learning and Computer Vision

Autonom ous Mining Equipment, Cameras and Sensors

Mining

Infosys

Machine Learning and

Cameras, Drones and Sensors

Mining

ed Technolog y

Nia

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Cognitive Computing Autono mous Robot for Gas & Oil Sites

Total

Machine Learning and Computer Vision and

Autonom Oil and ous Robots, Gas Cameras and Sensors

Unnam ed Technolog y

Gazprom Yandex

Machine Learning

Oil and Gas

Unnam ed Technolog y

Microsoft, Machine Baker Hughes and Learning C3.ai and Cloud Computing

Oil and Gas

Reactor Electric Power Rule Emergenc Research Institute Based y Alarm System Level Monitor (REALM)

Nuclear Power

GEMI NA Program and Humble AI

United States Machine Department of Learning Energy’s Advanced Research Projects Agency Energy Division, GE Research, MIT, Argonne National Laboratory and the University of Michigan

Nuclear Power

Virtual Environm

Electric Power Machine Research Institute Learning

Nuclear Power

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ent For Reactor Applicants (VERA) Unnam ed Technolog y

Korean Electric Artificial Power Company Neural Network

Unnam ed Technolog y

Lancaster University

Comput er Vision

NeurEc o Architectu re

Framatome and Machine ADAGOS Learning, Artificial Neural Network and Convolutio nal Neural Network

Nuclear Power

Autonom Nuclear ous Robots Power and Cameras ---

Nuclear Power

Unnam ed Technolog y

BirdVision

Artificial Neural Networks and Computer Vision

Cameras and Sensors

Unnam ed Technolog y

Tilt Renewables

Machine Learning

Autonom Wind ous Wind Power Generation Equipment

Unnam ed Technolog y

Google

Machine Learning, Deep Learning,

Wind Power

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Deep Neural Networks and Artificial Neural Networks SmartW ind Project

Zorlu Enerji

Machine Learning and Artificial Neural Networks

Wind Power

Unnam ed Technolog y

Vestas Wind Machine Systems and Learning Utopus Insights Inc.

Wind Power

Unnam ed Technolog y

Australian Machine Energy Market Learning Operator

Solar Power

Solcast Global Solar Forecastin g Applicatio n

New Solar

Unnam ed Technolog y

University Auvergne

Unnam ed Technolog

Avrio Energy, Machine Energly and The Learning Solar Labs

Energy

Machine Learning and Artificial Neural Networks

Autonom Solar ous Solar Power Generation Equipment

Machine Learning

Solar Panels

Solar Power

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y Unnam ed Technolog y Unnam ed Technolog y

Xcel Nnergix

and

OpenClimateFi x

Machine Learning

Solar Power

Machine Learning and Artificial Neural Networks

Solar Power

Unnam ed Technolog y

University of Deep Agder and Agder Learning Energi and Reinforce ment Learning

Hydrop ower

Unnam ed Technolog y

Skelleftea Kraft Machine AB Learning and Artificial Neural Networks

Hydrop ower

Unnam ed Technolog y

Voith and Acoustic Micropho Landsvirkjun Monitoring nes , Machine Learning and Big Data Analysis

Hydrop ower

Unnam ed Technolog y

Cornell University

Hydrop ower

Machine Learning

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Data Lake Insight

Inovia AI

Artificial Sensors Neural and Cameras Networks, Machine Learning and Internet of Things

Hydrop ower

ABB Solutions

ABB

Machine Sensors Learning, and Cameras Deep Neural Network and Internet of Things

Hydrop ower

Unnam ed Technolog y

Indian Institute Machine of Technology Learning Hyderabad

Biofuels

Unnam ed Technolog y

LanzaTech and Artificial TeslaGen Neural Networks and Cloud Computing

Biofuels

Unnam ed Technolog y

University Cordoba

Biofuels

Machine Learning, Artificial Neural Network, Convolutio nal Neural Network and Deep Learning

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The table above shows that the artificial intelligence technologies used in the global energy sector include machine learning, artificial neural networks, deep learning, computer vision and the like, sometimes combined with non- artificial intelligence fourth industrial revolution technologies such as cloud computing, big data analysis, blockchain and internet of things and also, industry specific hardware at times. Although various technologies already exist for safely obtaining energy from hazardous sources and in enhancing few renewable energy sources such as wind power, hydropower and solar power, extremely few technologies exist for biofuels since most artificial intelligence technologies in the subsphere are either at the nascent stage or at the experimental stage. Moreover, the most amount of technologies are seen in the electrical energy sphere but it is significantly less in the spheres of other forms of energy. Vulnerable artificial intelligence technologies have also led to incessant cyberthreats and grid collapses leading to severe dilemmas in the global energy sector. Although origins of convergence in the global energy sector with artificial intelligence can be traced back to the 1980s, regulatory mechanisms in the international sphere have been lackadaisical. This is mainly due to the fact that there are extremely few public international law conventional instruments in the sector and most of the global energy sector is regulated by private international law investment treaties and agreements. The looming cyberthreats and vulnerabilities of the artificial intelligence technologies that have severely affected the global energy sector over the years have also shown the need for lawmaking. Not only this, but the claim that artificially intelligent enabled tools and technologies are capable of reducing emissions and can enhance clean energy sources are itself challenged since these technologies themselves use humongous levels of energy to operate.

Outcome Analysis This section is dedicated towards showcasing the deficiencies in the areas of international energy governance with the help of existing unregulated advancements of artificial intelligence technologies. The illustration below shows the percentages of types of artificial intelligence technologies used in the global energy sector-

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Fig. 3. The percentages of types of artificial intelligence technologies used in the global energy sector

Therefore, in light of all the chapter sections above, it is apparent that the global energy sector has deeply and most prominently benefitted from machine learning and various neural networks followed by the remaining technologies and that reliance on hardware in the global energy sector seems to be wellbalanced. However, the chapter sections have also showcased that no specific regulations in the global energy sector for artificial intelligence technologies used exist in the international sphere. Moreover, the fact that most developers are either private sector companies or researchers from Universities and that, Government affiliated developers are rare, the need for sufficient multilateral, bilateral and regional legal instruments for capacity building, investment incentivisation, increased and cross- border development done keeping in mind relevant national standards and issues revolving around artificial intelligence technologies is currently the need of these times.

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International Space Law

Manohar Samal

Introduction Outer space has been one of the most interesting discoveries of human civilization and up till the present stage, extremely less amount of it has been understood. The advancements made in outer space research are still at its nascent stage and much work needs to be done in order to advance further studies, research and discoveries. Albeit the fact, the developments up till the present stage have not only been limited to discovery of celestial bodies in outer space but has also focused upon outer space activities which have improved services on Earth. The role of artificial intelligence is humongous in the outer space sector and by deployment of its technologies currently, insurmountable potential is being witnessed. Looking at this ever- increasing role, it goes without saying that a robust system of international space law which encompasses the technological aspects as well, exists, so that the use of artificial intelligence technology is exploited for the greater good of human civilization and for development of overall nations in a guided manner. Thus, in light of the aforesaid, the present chapter has been dedicated towards discussing the close nexus between outer space, artificial intelligence and international space law which will show the advancements in the field up till present times and will also highlight the deficiencies of the use of artificial intelligence in outer space and the role being played by international space law in such deployment.

Legal Background International space law has significantly developed under public international law, especially in terms of international legal instruments. However, due to the increase in commercialized activities and potential in outer space, the private international law is also being highlighted currently and, in a few years, will have an increased role and relevance due to the increase in occupations after increased commercial and noncommercial outer space activities which will bridge the gap of nationalities and country borders.

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Public International Law

Customary Law Principles. Over the span of time after the 1960s, increase in space activities started to take place which not only led to the introduction of international legal instruments to govern the relationship of countries in relation to space activities, but also led to the development of various customary international law principles. These include the limitations on the use of force in outer space, principle which barred the claim of sovereignty over celestial bodies, owner State’s responsibility for damage or trespass caused by space vehicles and equipment in another State and the treatment of astronauts as envoys (Scharf, 2013). The limitation on the use of force in outer space advocates for the peaceful use of outer space and in conducting space activities and can also be found under subsequently ratified international legal instruments. The original principle restricts military space operations and the placement of weapons in outer space in order to facilitate the goals of such limitations (Morozova, 2019). The principle barring the claim of nation sovereignty over celestial bodies is also found in subsequent international legal instruments and its purpose is indicative by its nomenclature (Turrini, 2019). Such form of restriction aimed at creating benefit for all the nations is not new and can also be found in respect to high seas (Moraes, 2019), the Arctic region and Antarctica (McKitterick, 1939). The principle creating State responsibility for damage or trespass caused by space vehicles and space objects to another State’s territory is also present in international legal instruments in the current era, but originally was introduced through international customary law (Matignon, 2019). The treatment of astronauts as envoys also originated as a customary international law principle but was later incorporated in the Outer Space Treaty (Cepelka, 1970). International Legal Instruments. A plethora of international legal instruments exist in the area of international space law. These include the 1967 Treaty on Principles Governing the Activities of States in the Exploration and Use of Outer Space, Including the Moon and Other Celestial Bodies (Outer Space Treaty), the 1968 Agreement on the Rescue of Astronauts, the Return of Astronauts and the Return of Objects Launched Into Outer Space (Rescue Agreement), the 1972 Convention on International Liability for Damage Caused by Space Objects (Liability Convention), the 1976 Convention on Registration of Objects Launched Into Outer Space (Registration Convention) and the 1984 Agreement Governing the Activities of States on the Moon and Other Celestial Bodies (Moon

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Agreement). Two significant Declarations and three important Principles also exist under international space law and these are the 1963 Declaration of Legal Principles Governing the Activities of States in the Exploration and Uses of Outer Space (Declaration of Legal Principles), the 1982 Principles Governing the Use by States of Artificial Earth Satellites for International Direct Television Broadcasting (Broadcasting Principles), the 1986 Principles Relating to Remote Sensing of the Earth from Outer Space (Remote Sensing Principles), the 1992 Principles Relevant to the Use of Nuclear Power Sources in Outer Space (Nuclear Power Sources Principles) and the 1996 Declaration on International Cooperation in the Exploration and Use of Outer Space for the Benefit and in the Interest of All States, Taking Into Particular Account the Needs of Developing Countries (Benefits Declaration). Private International Law. So far, only the United States of America, Japan and Luxembourg have enacted national level space legislation (Samal, 2020). However, the significant increase in contracts between private players with each other as well as between private players with States have led to the increased role of private international law in the space sector. Although the role of private international law may not be so apparent presently, within a span of few years and considering the rate at which advancements are taking place in the space sector due to the use of artificial intelligence, the role of private international law will be much larger than public international law in the years and decades to come.

Conjunction with Artificial Intelligence As highlighted above and is being reiterated under the present part, the role of artificial intelligence has been one of the greatest in the advancements of the space sector (Das, 2020). The exploration and usage of these technologies in the space sector have been explored since the 1960s (Scharf, 2013) and this has resulted in the use of artificial intelligence in risk analysis, planning of space missions, remote sensing, commercial collection of data obtained from space, commercial handling of data obtained from space, management, analysis, mapping and use of data collected from space, improvement in the production, deployment and use of space objects, vehicles and products, improvement in launching and returning facilities, improvement in energy management during space missions in space vehicles and objects, training of personnel, space mission support and space debris tracking.

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Case Studies on Applications of Artificial Intelligence in the Space Sector This section of the chapter is dedicated towards analyzing case studies of artificial intelligence applications in risk analysis, planning of missions, remote sensing, commercial collection, handling, management, analysis, mapping and use of data collected from space, production, deployment and use of space objects and products, launch and return facilities, energy management, training of personnel, mission support and space debris tracking which will help in establishing the intended results. Risk Analysis and Planning of Missions

Artificial intelligence technologies have been used for risk analysis and space mission planning. The Space Telescope Science Institute’s SPIKE program is an artificially intelligent software used for planning missions through scheduling searches solving various satellite scheduling problems (Johnston and Miller, 1992). Few other missions which use similar artificial intelligence based upon the SPIKE program include the Chandra, Subaru and FUSE Missions (Giuliano, 2018). Another successful risk analysis and mission planning tool has been the Mixed- Initiative Planning and Scheduling For the Mars Exploration Rover Mission (MAPGEN) which has used artificial intelligence to achieve its goals (Chang et al., 2004). Aurora is an artificial intelligence tool developed by Stottler Henke and has been used by NASA for its mission planning activities (Stottler Henke, 2020). The Space Mission Architecture and Risk Analysis Tool (SMART) (NASA Software, 2020) and the Space Architecture Failure Evaluation (SAFE) tool use artificial intelligence for planning of missions, risk analysis and in assessing the success rate and outcomes of space missions (NASA, 2020). Remote Sensing

Artificial intelligence has been significantly used for remote sensing activities. One of the many prominent artificial intelligence technologies leading in the field is the Autonomous Exploration for Gathering Increased Science System (AEGIS) which uses a wide span of artificially intelligent tools for remote sensing activities (NASA, 2009). One of the most prominent tools being used since a long span of years for the collection, management and analysis of data from space is the Sky Image Cataloging and Analysis Tool (SKICAT) which uses machine learning and rule based expert systems for its purposes (Chien and Morris, 2014). The European Space Agency’s ENVISAT used a variety of

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artificially intelligent hardware and software in order to indulge in remote sensing observations of Earth by the collection and analysis of environment, ocean and atmosphere related data (European Space Agency, 2019). Two private companies called LatConnect 60 and UP42 are planning to launch artificial intelligence enabled small constellations of smart satellites to improve remote sensing activities which will boost services on Earth (News Desk, 2020). Raytheon Intelligence & Space has developed Space & C2 Systems which uses machine learning and artificial neural networks in order to make satellites collect actionable intelligence resulting in the improvement of the data collection and dissemination to the right institute or person at the right time (Jewett and Holmes, 2020). The company C3S LLC in partnership with Almotive is also developing artificial intelligence tools on similar lines (Jewett and Holmes, 2020). Earthcube is a company which uses machine learning and deep learning tools to analyse and study remote sensing data (Allioux, 2018). Even an Indian private company called Pixxel has partnered with New Space India Ltd. (NSIL) in order to develop an artificially intelligent satellite which will boost remote sensing activities for India under the IN- SPACe program (The Hindu, 2020). Production, Deployment and Use of Space Objects and Products

Relativity Space is a rocket company which has used a combination of artificial intelligence and 3D printing to enhance the production of complex rocket parts (Salmi, 2019). The T- TAURI artificial intelligence system has been deployed by Lockheed Martin in order to improve the development and testing of satellites (Jewett and Holmes, 2020). Researchers from the University of Texas have been developing an artificially intelligent tool in order to improve the design and production process of rocket engines with the help of machine and reinforcement learning (Sagar, 2020). The Oden Institute for Computational Engineering and Sciences in partnership with the Massachusetts Institute of Technology is currently developing an artificial intelligence system which will help rocket builders in designing rockets more quickly and efficiently (Hurley, 2020). The European Space Agency, Intel and Ubotica have developed PhiSat-1 which is the first artificially intelligent satellite in the world (Das, 2020). Launch and Return Facilities

Lockheed Martin is currently working on an artificially intelligent tool which will improve the launch process and increase the number of launches of satellites which will also help the satellites to autonomously decide and plan their next maneuvers (Jewett and Holmes, 2020). The Epsilon Launch Vehicle developed by Japan Aerospace Exploration Agency uses artificial intelligence

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in order to improve the launch of rockets (Japan Aerospace Exploration Agency, 2003). Space X’s Crew Interactive Mobile Companion (CIMON) has been used in the Crew Dragon spacecraft for autopilot, automatic launch and return with the help of artificial intelligence (France- Presse, 2018). The European Space Agency in collaboration with the company Astrium is building a series of space vehicles called Automated Transport Vehicles- 2 (ATV2) which will function autonomously and perform various tasks including automatic launch and return (Press Trust of India, 2016). Training of Personnel and Mission Support Artificial intelligence has been used in the training of astronauts and astronomers as well. NASA’s virtual environment and artificial intelligence assisted software called Visual Environment For Remote Virtual Exploration (VERVE) has been utilised for a long time for training astronauts for space missions (NASA, 2020). Use of artificial intelligence for mission support has been far greater. The Orbital Express Mission uses artificial intelligence enabled Automated Scheduling and Planning Environment (ASPEN) tools for providing mission support through autonomous refueling of space vehicles and autonomous servicing of satellites launched in space (Chien and Morris, 2014). The Hyperdrive AI Services Integration Platform by Hypergiant aids space mission teams in monitoring, analyzing and assisting in managing large fleets of satellites significantly contributing to space mission support activities (Jewett and Holmes, 2020). Pony Express and La Jument are mission support apps made by Lockheed Martin and the University of Southern California which assist satellites in improving the images captured, sensors and also improves cybersecurity resilience (Jewett and Holmes, 2020). NASA’s Jet Propulsion Laboratory has partnered with the private company Akin in order to develop an artificially intelligent robot which will assist astronauts in deep space called Fiona (Patel, 2020). Space Debris Tracking One of the many space debris tracking systems has been developed by the company Hypergiant which is called the Hyperdrive AI Services Integration Platform which uses convolutional neural networks and machine learning which helps in tracking space debris which might pose a risk to launched satellites positioned in different areas of space. In fact the technology directly allows satellites to track space debris instead of having to rely upon ground based stations (Jewett and Holmes, 2020). One of the recently developed tools which has been successful for space debris tracking is the PHILOS- SOPHIA

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which uses artificial intelligence for achieving its purposes and has been created by the European Space Agency in collaboration with the Lawrence Livermore National Laboratory (Schimmerohn et al., 2018). The European Space Agency is also developing the first artificially intelligent robot which will have the task of clearing space debris from space in order to reduce the extant risk to satellites, rockets and spaceships which currently exists and this robot is known as the Clear Space- 1 Chaser (Macaulay, 2020). The UK Space Agency and the UK Ministry of Defence has awarded funding to several private companies in order to develop artificial intelligence robots, tools and software which will help in space debris cleanup and the companies which have been funded are Lift Me Off, Andor, Northern Space and Security, Deimos, Lumi Space and D- Orbit (Ingham, 2020). Researchers at the Chinese Academy of Surveying and Mapping, Fuxinhave and Beijing and Liaoning Technical University have used deep learning and deep neural networks in order to create a software capable of effective space debris identification and tracking (Yadav, 2019). Space Exploration The use of artificial intelligence in robotics has served to be quite useful in space exploration activities since robots and robotic vehicles are capable of working in hazardous and harsh environments (Townsend, 2019). The use of the artificial intelligence algorithm called Remote Agent along with the comet probe called Deep Space- 1 is one of oldest such examples which dates back to the year 1998 (Townsend, 2019). Nasa’s Solar Dynamics Observatory has been using artificial intelligence enabled sensors in order to study the influence of the Sun on the Earth and the space near Earth (Shekhtman, 2019). The Earth Observing Mission- 1 has also been using a variety of artificial intelligence technologies in order to achieve its goals (Chien and Morris, 2014). The Curiosity Rover uses onboard artificial intelligence and a system is also used back on Earth to analyse the data being sent by the rover from Mars (Bubenik, 2020). The Mars 2020 Rover also uses PIXL which heavily relies on artificial intelligence for exploration and collection of data on Mars (Prosser and Rebolledo, 2018). India’s Chandrayaan-2 Pragyan Rover also uses artificial intelligence for space exploration activities on the Moon (D’ Monte, 2019). NASA has partnered with Intel, IBM and Google to improve artificial intelligence algorithms for improving space exploration missions (AI Trends, 2020). NASA’s Goddard Space Flight Center and NASA’s Frontier Development Lab have partnered with the SETI Institute in order to carry out further experimentation on machine learning and neural networks in order to improve the use of artificial intelligence in space exploration missions and activities (AI Trends, 2020). NASA and Google have used artificial intelligence in the past to discover Kepler- 80g and Kepler- 90i which are exoplanets

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(Eduonix, 2019). Rollin Justin is an artificially intelligent robot which will help in improving studies and research on Mars (Eduonix, 2019). Artificial intelligence has also been used in the model being devised by Princeton University researchers called the Stability of Planetary Orbital Configurations Klassifier (SPOCK) which aims to assess if exoplanets will crash into other planets, celestial bodies or the stars of their system (Preetipadma, 2020).

Multi-Disciplinary Analysis This section has been dedicated towards analyzing the various technologies at play for artificial intelligence hardware and software being utilized for enhancement in the space sector and its convergence with international space law. Name of Develope Artificial r Intelligence Technology or Project

Artificial Supportin Intelligence g Hardware Software Type and Assisting Systems

Purpose

Remote Agent

NASA

Deep Neural Network, Artificial Neural Network and Machine Learning

Deep Space Space1 Exploration (Autonomous Comet Probe)

Solar Dynamics Observatory

NASA

Artificial Neural Network

Autonomo us Spacecraft

Earth Observing Mission- 1

NASA

Computer Vision, Machine Learning and Artificial Neural Network

Autonomo Space us Sciencecraft Exploration

Space Exploration

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SPIKE

Space Telescope Science Institute

Rule Based Expert System and Artificial Neural Network

Chandra Mission

NASA

Rule Based Expert System and Artificial Neural Network

Observator Risk y and X- Ray Analysis and Telescope Planning of Missions

FUSE Mission

NASA

Rule Based Expert System

Satellite and Risk Camera Analysis and Planning of Missions

Subaru Mission

National Astronomical Observatory of Japan

Rule Based Expert System and Artificial Neural Network

Automated Scheduling and Planning Environment (ASPEN) used in the Orbital Express Mission

United States of America Defense Advanced Research Projects Agency and NASA

Machine Learning, Artificial Neural Network and Computer Vision

Sky Image Institute Cataloging and for Analysis Tool Astronomy, (SKICAT) Hawaii

Machine Learning and Rule Based Expert System

Curiosity Rover (Mars Exploration

Machine Learning, Artificial Neural

NASA

Telescope

Risk Analysis and Planning of Missions

Autonomo Mission us Space Support Refueling Vehicle and Autonomous Satellite Servicing Vehicle --

Remote Sensing

Rover and Space Camera Exploration

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Rover Mission [MERS])

Networks, Image and Object Recognition and Computer Vision

MixedInitiative Planning and Scheduling For the Mars Exploration Rover Mission (MAPGEN)

NASA

Machine Learning and Artificial Neural Network

Autonomou s Exploration For Gathering Increased Science System (AEGIS)

NASA Jet Propulsion Laboratory and California Institute of Technology

Computer Vision, Image and Object Recognition and Machine Learning

Cameras and Rovers

Risk Analysis and Planning of Missions

Remote Sensing

ENVISAT

European Computer Space Agency Vision and Machine Learning

Satellite and Remote Rocket Sensing

PIXL

NASA Jet Computer Propulsion Vision, Image Laboratory and Object Recognition and Machine Learning

Mars 2020 Space Rover Exploration

Hyperdrive Hypergian AI Services t Integration Platform

Machine Learning and Convolutional Neural Network

Satellite

Risk Analysis, Planning of Missions and Space Debris Tracking

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Unnamed Technology

Relativity Space

Unnamed Technology

UP42 and Machine LatConnect Learning, 60 Artificial Neural Network, Computer Vision and Supercomputi ng

T- TAURI

Lockheed Martin

Machine Learning and 3D Printing

Machine Learning

Pony Lockheed Machine Express and La Martin and Learning and Jument University of Image Southern Recognition California Unnamed Technology

Lockheed Martin

Space & C2 Raytheon Systems Intelligence & Space

Machine Learning Machine Learning and Artificial Neural Network

3D Printers

Productio n, Deployment and Use of Space Objects and Products

Smart Remote Satellite and Sensing Supercompute r

Productio n, Deployment and Use of Space Objects and Products

Satellite

Mission Support

Autonomo us Satellite Satellite

Launch and Return Facilities Remote Sensing

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Unnamed Technology

Aurora

C3S LLC Machine and Almotive Learning and Artificial Neural Network

Satellite

Remote Sensing

Stottler Henke

Machine Learning and Artificial Neural Network

Risk Analysis and Mission Planning

Unnamed Technology

Earthcube

Machine Learning and Deep Learning

Remote Sensing

Unnamed Technology

University of Texas

Machine Learning and Reinforcement Learning

Productio n, Deployment and Use of Space Objects and Products

Epsilon Launch Vehicle

Japan Aerospace Exploration Agency

Machine Learning and Artificial Neural Network

Unnamed Technology

Oden Institute For Computation al Engineering and Sciences and Massachusett s Institute of Technology

Machine Learning and Artificial Neural Network

Rocket and Launch Rocket and Return Launch Facilities Vehicle --

Productio n, Deployment and Use of Space Objects and Products

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PhiSat- 1

Autonomo us Satellite

Productio n, Deployment and Use of Space Objects and Products

Crew Dragon Spacecraft

Launch and Return Facilities

Automated European Artificial Transport Space Agency Neural Vehicles2 and Astrium Network, (ATV2) Machine Learning and Computer Vision

Space Vehicles

Launch and Return Facilities

Chandrayaa n-2 Pragyan

Indian Space Research Organisation

Space Rover

Space Exploration

Unnamed Technology

Pixxel and Computer New Space Vision and India Ltd. Machine Learning

Satellite

Remote Sensing

Fiona

NASA Jet Computer Propulsion Vision, Image

Robot

Mission Support

Crew Interactive Mobile Companion (CIMON)

European Computer Space Vision, Image Agency, Intel Recognition and Ubotica and Object Recognition

Space X

Artificial Neural Network, Machine Learning, Computer Vision, Image Recognition and Object Recognition

Computer Vision, Image and Object Recognition and Artificial Neural Network

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Laboratory and Akin

and Object Recognition, Facial Recognition and Deep Learning

NASA

Machine Learning and Virtual Reality

Training of Personnel

Space NASA Mission Software Architecture and Risk Analysis Tool (SMART)

Rule Based Expert System and Machine Learning

Risk Analysis and Planning of Missions

Space Architecture Failure Evaluation (SAFE)

Rule Based Expert System and Machine Learning

Risk Analysis and Planning of Missions

European Rule Based Space Agency Expert System and and Machine Lawrence Learning Livermore National Laboratory

Space Debris Tracking

Robot

Space Debris Tracking

Visual Environment For Remote Virtual Exploration (VERVE)

PHILOSSOPHIA

NASA

Clear SpaceEuropean Machine 1 Chaser Space Agency Learning, Artificial Neural Network, Computer Vision, Image

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and Object Recognition Unnamed Technology

Chinese Deep Academy of Learning and Surveying Deep Neural and Mapping, Network Beijing and Liaoning Technical University and Fuxinhave

Space Debris Tracking

Unnamed Technology

UK Space Agency, UK Ministry of Defense, Lift Me Off, Andor, Northern Space and Security, Deimos, Lumi Space and D- Orbit

Machine Learning, Artificial Neural Network, Computer Vision, Image and Object Recognition

Space Debris Tracking

Unnamed Technology

NASA, Machine Google, Intel Learning and and IBM Artificial Neural Network

Space Exploration

Unnamed Technology

NASA’s Goddard Space Flight Center, NASA’s Frontier Development Labs and

Space Exploration

Machine Learning and Artificial Neural Network

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SETI Institute Unnamed Technology

NASA and Google

Artificial Neural Network

Space Exploration

Rollin Justin

German Aerospace Center and Institute of Robotics and Mechatronics

Artificial Neural Network, Machine Learning and Computer Vision

Robot

Space Exploration

SPOCK

Princeton University

Machine Learning and Artificial Neural Network

Space Exploration

The table above showcases that artificial intelligence technologies being used in the space sector include a variety of tools such as deep learning, machine learning, artificial neural networks, deep neural networks, computer vision and the rule based expert system which are sometimes combined with non- artificial intelligence fourth industrial revolution technologies such as virtual reality and 3D printing. A significant amount of hardware is used with a combination of artificial intelligence software in the space sector which has led to the current developments and the possibility of envisioning future developments in the sector. Although artificial intelligence technologies exist for most of the activities related to the space sector, it can be seen from the results in the case studies that few artificially intelligent tools have been developed for training of personnel for space missions, energy management of space vehicles and for launch and return facilities. In terms of governance related aspects, it is unfortunate that nations have not enacted domestic level legislation and are relying upon the soft international legal instruments which have been ratified by them for enhancing advancements in the space sector. Under such a scenario, a legal framework for artificial intelligence technologies involved in the space sector seems to look like a distant dream. There are no legally enforceable instruments under the international sphere which are capable of addressing the problems which can

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arise out of artificial intelligence deployment in the space sector (Soroka and Kurkova, 2019). It seems to be clear that the involvement of Governments and Government- affiliated organisations have been significant in the expansion and development of the space sector. Despite this fact, all the multilateral legal instruments signed by Governments do not directly pertain to artificial intelligence technologies in any manner and only seem to focus on the general principles and law required for smooth development of the space sector. Looking at the rate of space commercialisation, the non- existence of a robust international framework which deals with all the aspects related to use of artificial intelligence in space may open Pandora’s box in the near future.

Outcome Analysis This section is dedicated towards showcasing the deficiencies in the areas of international space governance with the help of existing unregulated advancements of artificial intelligence technologies. The illustration below shows the percentages of types of artificial intelligence technologies used in the space sector-

Fig. 4. The percentages of types of artificial intelligence technologies used in the space sector

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Therefore, in light of all the chapter sections above, it is apparent that the space sector has deeply and most prominently benefitted from machine learning followed by neural networks and the remaining listed technologies and that reliance on hardware in the space sector is significant and much greater than other sectors. However, the chapter sections have also showcased that no specific regulations for space specific artificial intelligence technology exists in the international sphere. Moreover, the fact that most developers are either Governments, Government- affiliated organisations and universities and the fact that private players have only entered since the past half decade, the need for an adequate international space law instrument or guiding principles on artificial intelligence technologies used in the space sector is the need of the present time.

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International Civil Aviation Law

Manohar Samal

Introduction Civil aviation refers to the use of aircraft for either personal or for business related purposes including a variety of activities under its ambit, such as the transportation of people or goods, specifically excluding the use of aircraft for military purposes (Cambridge Dictionary, 2020). The International Civil Aviation Organisation is the body which attempts to establish cooperation and diplomacy amongst the countries in the world but is not a regulator of international civil aviation law (International Civil Aviation Organisation, 2020). In fact the contribution of the International Air Transport Organisation has been significant in terms of adding value to international civil aviation law due to the fact that, it is a trade association of airline companies which intricately understand the business and trade customs of the civil aviation industry (IATA, 2020). The contribution of civil aviation in the global Gross Domestic Product (GDP) is at 3.6% with a monetary value of USD 2.7 Trillion (Asquith, 2020). The contribution of artificial intelligence has been insurmountable in transforming the global civil aviation sector and its future potential has also been recognized due to its application in the development of various civil aviation aspects (International Civil Aviation Organisation, 2019) such as inflight controls, training of personnel, improvement of flight technology, accident avoidance, customer service automation, improvement of the civil aviation infrastructure, improvement in the availability and delivery of support services to civil aviation and in enhancing sustainability in civil aviation. Looking at this ever- increasing role of artificial intelligence in the global civil aviation sector, it goes without saying that a robust system of international civil aviation law needs to exist which encompasses artificial intelligence related issues, so that the use of artificial intelligence technology is exploited for the enhanced development of the global civil aviation sector. Thus, in light of the aforesaid, the present chapter has been dedicated towards discussing the close nexus between the global civil aviation sector, artificial intelligence and international civil aviation law which will show the advancements in the field up till present times and will also highlight the deficiencies of the use of artificial intelligence in global civil aviation and the role being played by international civil aviation law in such deployment.

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International civil aviation law is not limited to any sub- discipline of international law and can be found under private international law and can also be equally found under public international law. This part of the chapter is dedicated towards discussing the various sources of international civil aviation law.

Public International Law Customary Law. Customary international law in international civil aviation has been developing since the 1920s and most of the customs have been incorporated within international legal instruments. The two most prominent international civil aviation customary law principles are the principle of airspace sovereignty and the principle of freedoms of the air (Mateou, 2017). The principle of airspace sovereignty indicates the control of nations over their own territorial airspace (International Civil Aviation Organisation, 2013). However, considering the fact that one of the major quintessentials of civil aviation is the seamless flying from one territory to another (International Civil Aviation Organisation, 2013), the principle of freedoms of the air was propounded (American Historical Association, 2020). The customary international law principle of the freedoms of the air contains five freedoms which most of the countries in the world practice and these are the freedom to fly across the territories of other countries without landing, the freedom to land for non- traffic purposes, the freedom to put down mail, cargo and passengers taken in the territory of the country whose nationality the aircraft possesses, the freedom to take on cargo, mail and passengers destined for the territory of the country whose nationality the aircraft possesses and the freedom to take on mail, cargo and passengers destined for the territory of another agreeing nation and to put down passengers, cargo and mail coming from any such territory (American Historical Association, 2020). International Legal Instruments. Over the span of time, various international legal instruments have developed in international civil aviation law. These include the Convention on Offences and Certain Other Acts Committed on Board Aircraft, 1963 (Tokyo Convention), Convention for the Suppression of Unlawful Seizure of Aircraft, 1970 (Hague Convention), Convention for the Suppression of Unlawful Acts Against the Safety of Civil Aviation, 1971 (Montreal Convention), Convention Relating to the Regulation of Aerial Navigation, 1919 (Paris Convention), Convention on International Civil Aviation, 1944 (Chicago Convention), Convention on the Suppression of Unlawful Acts Relating to International Civil Aviation, 2010 (Beijing Convention) and the Convention on the Marking

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of Plastic Explosives for the Purpose of Detection, 1991 (MEX Convention).

Private International Law Private international law aspects in international civil aviation are directly related with airline staff, airline companies and passengers and also extend over to the choice of jurisdiction, choice of law and choice of dispute resolution forum decided under various aviation purchase, sale, leasing and other contracts which exist worldwide (Verplaetse, 1958). In fact due to this, various private international law multilateral legal instruments have emerged such as the Convention on International Interests in Mobile Equipment, 2004 (Cape Town Convention), Convention for the Unification of Certain Rules Relating to International Carriage by Air, 1929 (Warsaw Convention), Montreal Convention, 1999, Convention on Damage Caused by Foreign Aircraft to Third Parties on the Surface, 1952 (Rome Convention), Convention on Compensation for Damage Caused by Aircraft to Third Parties, 2009 (General Risks Convention), Convention on the International Recognition of Rights in Aircraft, 1948 (Geneva Convention) and the Convention on Compensation for Damage to Third Parties Resulting from Acts of Unlawful Interference Including Aircraft, 2009 (Unlawful Interference Compensation Convention).

Conjunction with Artificial Intelligence

Artificial intelligence technologies are being paramountly utilised in the global civil aviation sector in order to not only improve flight technology and civil aviation infrastructure, but also to provide a variety of automated and efficient services to its customers which has also benefited the support services sector in civil aviation (AIT Staff Writer, 2020). In fact, even the International Civil Aviation Organisation has considered artificial intelligence technologies to be the game changers which will transform the global civil aviation sector in a positive way (International Civil Aviation Organisation, 2019). The continuous stakeholder consultation by various international and regional civil aviation related organisations has shown concrete efforts which will ultimately lead to artificial intelligence regulation which seems to be missing in many other emerging sectors. It has also been witnessed that such efforts will go a long way in paving the path to boosted development and usage of artificial intelligence technologies in the global civil aviation sector.

Case Studies on Application of Artificial Intelligence in the Civil Aviation Sector This section of the chapter is dedicated towards analyzing case studies of artificial intelligence applications on inflight controls, training of personnel, flight technology improvement, accident avoidance, customer service automation, fleet management, crew management, improvement of the civil

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aviation infrastructure, improving support services and in increasing sustainability in activities related to civil aviation in the world, that will help in establishing the intended results.

In-Flight Controls, Training, Flight Technology and Accident Avoidance Artificial intelligence technologies have been deployed for the development of inflight controls, improvement of overall flight technology and in reducing the chances of accidents in civil aviation. SynapseMX Inc. has developed an artificial intelligence platform which automates the monitoring of technical and maintenance activities which helps in quicker and more efficient decision making (Altexsoft, 2018). The engineers and other technical staff can even evaluate past failures and analyse alternative solutions which will help in achieving effective maintenance activities with the help of this artificial intelligence platform (Altexsoft, 2018). The airline company called EasyJet uses Skywise, which is an artificially intelligent software which assists in effective aircraft maintenance, leading to the reduction in chances of accidents due to lackadaisical maintenance of aircrafts (Altexsoft, 2018). Southwest Airlines has been working with NASA in order to develop an artificially intelligent tool which will study real time data of aircrafts in the air to reduce the chances of accidents and improve safety in air (Altexsoft, 2018). The company Airbus has been using Runway Overrun Prevention System (ROPS) using artificial intelligence for improving flight technology to conduct compatibility checks such as the length of the runway, weather conditions, weight of the aircraft, approach speed and is also capable of applying automatic brakes of the aircraft (Balaganur, 2020). NASA’s Dryden Flight Research Centre, NASA Ames Research Centre, Boeing Phantom Works, West Virginia University and Georgia Institute of Technology have collaboratively developed the Intelligent Flight Control System (IFCS) which has not only helped in improving the autopilot functions of military aircraft but has also significantly contributed to civil aviation autopilot functions (Rodriguez, 2020). A United Kingdom based company called FliteTrak has introduced Viator Aero which uses artificial intelligence to improve flight technology and safety by providing real time data on seat belts status, mobile phone activation status and similar such information (Aviation Business News, 2018). The company Garmin has introduced Telligence, an artificial intelligence tool which improves flight technology and reduces the burden over pilots by allowing them to control certain functions of the aircraft through voice commands and to interact with the aircraft during the duration of the flight for receiving information and data (BAA Training, 2018). Garmin has also developed the Electronic Stability and Protection System which uses artificial intelligence to check the altitude and

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angle of the aircraft and automatically takes control to put the aircraft in a safe position (BAA Training, 2018). The company DARPA has invented Aircrew Labour- in- Cockpit Automation System (ALIAS) which is an artificially intelligent robotic arm which is capable of replacing the co- pilot since it can push and pull buttons as per instructions (BAA Training, 2018). Training. The global flight school called CAE has invested USD 1 Billion in artificial intelligence technologies to improve training of their pilots and in order to improve the assessment system for pilots in training which will also increase automated training processes in the flight school (Baranick, 2018). A company called Paladin AI is in the process of developing its artificially intelligent InstructIQ in order to impart adaptive training for pilots which will improve efficiency in the civil aviation industry and also reduce costs of imparting training to pilots (Klassen, 2020). Flight Safety International has developed an artificial intelligence tool called FlightSmart in collaboration with IBM which uses artificial intelligence to evaluate civil aviation pilots and critically assess and test their capabilities in all phases of the flying (Mark, 2019). The company L3 Harris has introduced RealitySeven which is a training simulation solution used for training civil aviation pilots with the help of artificial intelligence (L3 Harris, 2020). It is capable of training in several cockpits, reducing power usage and reducing costs for airline companies and airline training centers and schools (L3 Harris, 2020).

Customer Service Automation, Fleet and Crew Management Artificial intelligence technologies have been deployed for the development of customer service automation, fleet and crew management as well which has led to fruitful results for airline companies. Customer Service Automation. PROS Airline Revenue Management Software is an artificially intelligent software which studies huge amounts of data such as customer preferences, demanded flight routes and similar information in order to sell the correct airline ticket to the correct person at the right time for the right costs (Altexsoft, 2018). The software is also capable of ancillary price optimisation and in calculating the expected marginal seat revenue (Altexsoft, 2018). A company called Pure Strategy has introduced the Automated Neural Intelligence Engine which uses artificial intelligence in order to take feedback from customers which it analyses and presents before airline companies so that such airline companies can rethink their customer service strategies (Altexsoft, 2018). Another artificial intelligence application called Coseer developed by Arbot Solutions automates messages and email responses from airline companies to

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the customers when they pose different queries and this technology has proved to be extremely useful in times of flight delay (Altexsoft, 2018). Southwest Airlines uses an artificial intelligence software developed by Aspect called Automatic Call Distribution System which helps with workforce optimisation which leads to the improvement of overall customer service (Altexsoft, 2018). Air New Zealand has also introduced its artificial intelligence chatbot called Oscar which has significantly improved automated customer service (Air New Zealand, 2020). Delta Airlines has invested in an artificially intelligent automated self- service bag checking kiosk which has gained popularity amongst its customers (Sennaar, 2019). TAV Technologies has introduced an artificial intelligence tool called Flight Delay Prediction Solution which predicts potential delays in flights and has proved to be useful to companies (TAV Technologies, 2020). Airport AI has developed an artificial intelligence platform in its own name for airports that help with customer service automation such as solving customer queries, collecting relevant business related data and has self- learning capabilities to suit the special needs of each and every airport (Airport AI, 2020). Similar technology has been developed by Zensors in collaboration with Carnegie Mellon University (Zensors, 2019). Artificial intelligence has come to the rescue of airports and airline companies after the outbreak of the coronavirus pandemic as well. This is evident from the artificial intelligence tools deployed by FlightHub and JustFly which helped them in automating refunds to customers whose flights were cancelled due to restrictions placed by countries after the pandemic’s outbreak which created a seamless system eliminating the requirement of agents and aiding quicker processing of refunds (Mulfati, 2020). Furthermore, AirAsia’s AVA and AirChat also helped customers in making decisions which helped in implementing pandemic safeguards in airports and aircrafts with the help of artificial intelligence (Mulfati, 2020). The Incheon International Airport Corporation has partnered with LG and has developed an artificially intelligent robot which helps customers in the airport terminal (Terminal Design, 2018). Fleet and Crew Management. The company called Jeppesen has developed an artificial intelligence solution called Crew Rostering which helps in effective crew management and scheduling of flights on a daily basis with the help of predictive analysis, passenger data, maintenance related information and similar such information (Altexsoft, 2018). The Delta Airlines company has been using three software such as SmartSignal, Bit Stew and Asset Performance Management in order to effectively ensure fleet management (Altexsoft, 2018). Sichuan Airlines has introduced its own artificially intelligent ground support system to improve crew management and operations (Xinhua, 2020).

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Civil Aviation Infrastructure An air traffic management service company called NATS has tested its artificially intelligent tool at Heathrow Airport in order to test if machine learning and computer vision can help in better landing facilities during low visibility in times of bad weather (Ahlgren, 2020). The company called Synapse Technology has developed an artificial intelligence based X- ray machine called Syntech ONE 200 which has been placed in various airport check- point scanners to improve airport security (Ahlgren, 2020). The Frankfurt Airport has introduced an artificially intelligent robot called YAPE, developed by Yape SRL which allows customers to interact with the robot using their smartphone and permits them to keep small luggage on the robot (Robotics & AI, 2019). YAPE is also capable of guiding customers to their respective gates due to its in- built airport navigation system (Robotics & AI, 2019). The British Airways in collaboration with the Munich Airport has partnered with the company BotsAndUs in order to develop an artificially intelligent autonomous robot to enhance passenger punctuality (Initiatives, 2020). Artificial intelligence has also been used to improve baggage logistics in the Hague Airport and in Hong Kong International Airport (Initiatives, 2020). This has been done by the company Vanderlande by the introduction of its artificially intelligent tool called FLEET which has increased baggage management efficiency and has made the work of baggage staff easier at airports (Initiatives, 2020). Delta Airlines has partnered with Georgia Tech and Curiosity Lab to test the production and deployment of artificially intelligent autonomous buses, trucks and cars at airports (Initiatives, 2020). Even the Frankfurt Airport has partnered with Hybrid- Airplanes Technologies GmbH to develop artificially intelligent aerial vehicles which will assist in doing status checks at airport terminals (Robotics & AI, 2019). Edmonton International Airport has entered into a partnership with Drone Delivery Canada in order to become the first airport in the world to become a hub for drone cargo deliveries (Initiatives, 2020). Presently, the SITA Lab is testing an airport digital twin at the United States of America East Coast Airport which will aid quicker decision making and improve efficiency in airport operations (Initiatives, 2020). All Nippon Airways and Panasonic have partnered in order to test the latest generation of selfdriving, artificially intelligent wheelchairs to help passengers with limited mobility capacity (Initiatives, 2020). Similarly, Etihad Airways and Abu Dhabi Airport have already tested self- driving, autonomous wheelchairs (Initiatives, 2020). Emirates Flight Catering and Winnow have created an artificial intelligence tool in partnership which has the capability of reducing flight food wastage as high as 35% (Saunders, 2020). The China Eastern Airlines has used

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artificial intelligence successfully in order to collect preferences and information from customers in order to provide them with personalized food as per their preferences on board their flights (Roy, 2019). MITRE has been developing an artificially intelligent tool for efficiently extracting information from air traffic control chatter by speech recognition and conversion of the speech to text to improve decision making in civil aviation (MITRE, 2019). The United Arab Emirates Civil Aviation Authority has partnered with Searidge Technologies to develop an artificially intelligent tool for improving air traffic management (Marr, 2019). The International Air Transport Association has collaborated with the Airports Council International to launch the New Experience in Travel and Technologies (NEXTT) Initiative which uses artificial intelligence and predictive analysis in order to study behaviour of people in the airport that helps in increasing airport security (Blum, 2020). The Aberdeen Airport has used Finnish company Vaisala’s artificial intelligence tool called VIOMINER in order to monitor cracks, potholes and abrasions in airport pavements and runways (Soler, 2019).

Sustainability in Civil Aviation Artificial intelligence technologies have helped in improving environmental sustainability in the global civil aviation sector. One of the most prominent examples is the use of GE Aviation’s artificial intelligence system by Southwest Airlines in order to improve fuel efficiency and consumption for entire fleets of aircraft (Altexsoft, 2018). Air France and Open Airlines have collaborated together to introduce SkyBreathe, which uses artificial intelligence to cut carbon dioxide emissions, save fuel and boost efficiency, contributing to sustainability (DailyAlts, 2020). A company called Safety Line has introduced its artificially intelligent tool which is capable of monitoring the climb profiles of aircrafts and suggest the pilots some techniques in which better fuel efficiency can be achieved. This is extremely vital since aircrafts consume the most fuel while the climbing phases and this tool has already been used by Air Austral (AMFG, 2018). Airbus has introduced NAVBLUE which is a dynamic flight path optimization artificially intelligent solution used for flight path optimization and efficient navigation in a manner which saves fuel in a significant way (Wyman, 2018). The company StorkJet also uses similar artificial intelligence technology in order to save fuel (Aircraft IT, 2020).

Multi-Disciplinary Analysis

This section has been dedicated towards analyzing the various technologies at play for artificial intelligence hardware and software being utilized for enhancement in the global civil aviation sector and its convergence with international civil aviation law.

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Name of Artificial Intelligence Technology or Project

Developer

Artificial Intelligence Software Type and Assisting Systems

Supporting Hardware

PROS Airline PROS Revenue Management Software

Machine Learning

Unnamed Technology

SynapseMX Inc.

Machine -Learning and Artificial Neural Networks

InFlight Controls, Flight Technology and Accident Avoidance

Automated Neural Intelligence Engine

Pure Strategy

Machine -Learning and Convolutional Neural Network

Customer Service Automation

Coseer

Arbot Solutions

Machine -Learning and Natural Language Processing

Customer Service Automation

Crew Rostering

Jeppesen

Machine Learning

Fleet and Crew Management

Machine Learning

Fleet and Crew Management

Bit Stew and General Asset Electric Performance Management

Purpose

Customer Service Automation

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SmartSignal

General Electric

Machine Learning

Fleet and Crew Management

Skywise

Machine -Learning and Artificial Neural Network

InFlight Controls, Flight Technology and Accident Avoidance

Machine Learning

Customer Service Automation

Automatic Call Aspect Distribution System

Unnamed Technology

NASA and Machine -Southwest Learning, Airlines Artificial Neural Networks and Big Data Analytics

InFlight Controls, Flight Technology and Accident Avoidance

Unnamed Technology

NATS

Machine Ultra HD 4K Civil Aviation Learning, Image Cameras Infrastructure Recognition, Object Recognition and Computer Vision

Unnamed Technology

GE Aviation

Machine -Learning and Big Data Analytics

Sustainability in Civil Aviation

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Syntech ONE Synapse 200 Technology

Image and Xray Civil Aviation Object Machines Infrastructure Recognition, Computer Vision and Machine Learning

Runway Overrun Prevention System

Machine -Learning and Artificial Neural Network

InFlight Controls, Flight Technology and Accident Avoidance

Artificial Neural -Network, Dynamic Neural Network, Static Neural Network and Computer Vision

InFlight Controls, Flight Technology and Accident Avoidance

Airbus

Intelligent NASA Flight Control Dryden System Flight Research Centre, NASA Ames Research Centre, Boeing Phantom Works, West Virginia University and Georgia Institute of Technology Oscar

Air New Machine -Zealand Learning, Natural Language Processing and Artificial Neural Network

Customer Service Automation

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Unnamed Technology

Delta Airlines

Facial Baggage Recognition, Kiosk Object Recognition and Computer Vision

Customer Service Automation

Flight Delay TAV Machine -Prediction Technologies Learning and Solution Artificial Neural Network

Customer Service Automation

ViatorAero

FliteTrak

Machine Sensors Learning, Object Recognition and Internet of Things

InFlight Controls, Flight Technology and Accident Avoidance

Unnamed Technology

CAE

Machine -Learning, Virtual Reality, Cloud Computing and Augmented Reality

Training

InstructIQ

Paladin AI

Deep Neural -Network

Training

Artificial Neural -Network, Natural Language Processing and Machine Learning

InFlight Controls, Flight Technology and Accident Avoidance

Electronic Garmin Stability and Protection System

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Garmin

Aircrew DARPA LabourinCockpit Automation System (ALIAS)

Machine -Learning, Artificial Neural Network and Natural Language Processing

InFlight Controls, Flight Technology and Accident Avoidance

Natural Robotic Arm Language Processing, Artificial Neural Network, Computer Vision, Object Recognition and Robotics

InFlight Controls, Flight Technology and Accident Avoidance

FlightSmart

Flight Safety Machine -International Learning, and IBM Artificial Neural Network and Big Data Analytics

Training

RealitySeven

L3 Harris

Deep Neural Flight Network, Deep Simulator Learning, Virtual Machine Reality and Augmented Reality

Training

Airport AI

Machine -Learning and Big Data Analytics

Customer Service Automation

Unnamed Technology

FlightHub

Machine -Learning, Natural Language Processing and Big Data Analytics

Customer Service Automation

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Unnamed Technology

JustFly

Machine -Learning, Natural Language Processing and Big Data Analytics

Customer Service Automation

AVA

AirAsia

Machine -Learning, Natural Language Processing and Big Data Analytics

Customer Service Automation

AirChat

Machine -Learning and Natural Language Processing

Customer Service Automation

Unnamed Technology

Zensors and Carnegie Mellon University

Machine -Learning and Natural Language Processing

Customer Service Automation

YAPE

Yape SRL

Artificial Neural Autonomous Civil Aviation Network, Robot and Infrastructure Computer Sensors Vision, Image and Object Recognition and Internet of Things

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Airstar Robot

Incheon International Airport Corporation and LG

Artificial Neural Autonomous Network, Robot Machine Learning, Computer Vision, Image and Object Recognition and Natural Language Processing

Customer Service Automation

Unnamed Technology

Munich Airport, British Airways and BotsAndUs

Artificial Neural Autonomous Network, Robot Machine Learning, Computer Vision, Image and Object Recognition and Natural Language Processing

Civil Aviation Infrastructure

FLEET

Vanderlande

Artificial Neural -Network and Machine Learning

Civil Aviation Infrastructure

Unnamed Technology

Delta Airlines, Curiosity Lab and Georgia Tech

Deep Learning, Deep Neural Network, Computer Vision, Image and Object Recognition and Internet of Things

Civil Aviation Infrastructure

Autonomous Cars, Autonomous Trucks and Autonomous Buses

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H- Aero

Frankfurt Airport and HybridAirplane Technologies GmbH

Deep Learning, Autonomous Deep Neural Aerial Network, Vehicle Computer Vision, Image and Object Recognition and Internet of Things

Civil Aviation Infrastructure

Unnamed Technology

Edmonton International Airport and Drone Delivery Canada

Artificial Neural Autonomous Network, Drones Computer Vision, Image and Object Recognition and Internet of Things

Civil Aviation Infrastructure

Machine -Learning, Artificial Neural Network and Big Data Analytics

Civil Aviation Infrastructure

Digital Twin SITA Lab Initiative

Unnamed Technology

All Nippon Deep Learning, Autonomous Airways and Computer Wheelchair Panasonic Vision, Image and Object Recognition and Internet of Things

Civil Aviation Infrastructure

Unnamed Technology

Etihad Airways and Abu Dhabi Airport

Civil Aviation Infrastructure

Deep Learning, Autonomous Computer Wheelchair Vision, Image and Object Recognition and Internet of Things

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Unnamed Technology

Winnow and Emirates Flight Catering

Machine -Learning and Artificial Neural Network

Civil Aviation Infrastructure

Unnamed Technology

China Eastern Airlines

Machine -Learning and Big Data Analytics

Civil Aviation Infrastructure

Unnamed Technology

Sichuan Airlines

Machine -Learning and Artificial Neural Network

Fleet and Crew Management

Unnamed Technology

MITRE

Deep Learning -and Natural Language Processing

Civil Aviation Infrastructure

New Experience in Travel and Technologies (NEXTT) Initiative

International Air Transport Association and Airports Council International

Computer Vision Machine Learning

Civil Aviation Infrastructure

VIOMINER

Vaisala

Machine -Learning, Cloud Computing and Big Data Analytics.

Civil Aviation Infrastructure

SkyBreathe

Air France Deep Learning -and Open and Deep Neural Airlines Network

Sustainability

Cameras and

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Unnamed Technology

Safety Line

Machine -Learning and Artificial Neural Network

Sustainability

NAVBLUE

Airbus

Machine -Learning and Artificial Neural Network

Sustainability

Unnamed Technology

StorkJet

Machine -Learning and Artificial Neural Network

Sustainability

The table above showcases that artificial intelligence in civil aviation has used a wide variety of technologies such as artificial neural networks, deep neural networks, deep learning, machine learning, computer vision and natural language processing. Furthermore, some artificially intelligent tools, software and platforms have also been combined with non- artificial intelligence fourth industrial revolution technologies such as the internet of things, big data analytics, virtual reality, augmented reality and cloud computing to achieve their objectives. In fact, some technologies have also seen synthesis between artificially intelligent software and hardware, especially in cases of self- check in kiosks, baggage and security scanners, autonomous robots, autonomous vehicles and self- driving wheelchairs. The results above also showcase that artificial intelligence technologies have not been sufficiently developed for some civil aviation related activities such as training of pilots, fleet management and crew management and minimum amount of technology exists in this sphere. Furthermore, artificial intelligence technologies which enhance sustainability in aviation only focus on the fuel efficiency aspect and ignore various other sustainability related aspects. In terms of governance, few steps are being actively taken by international and regional level actors in order to regulate artificial intelligence used in civil aviation. Notable examples include regulatory and white papers by the International Air Transport Association (IATA, 2018), working papers, conferences and assemblies by International Civil Aviation Organisation (ICAO, 2019) and the studies conducted by the European Parliament in respect

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of legal affairs and civil liability associated with the use of artificial intelligence (European Parliament, 2020). Furthermore, the European Union Aviation Safety Agency has been consulting relevant stakeholders in civil aviation in order to make rules and regulations for the use and deployment of artificial intelligence in civil aviation since the past few years (Soudain, 2020) which has resulted into the creation of the Artificial Intelligence Roadmap 1.0 by them (EASA, 2020). In pursuance of this, it seems to be clear that governance and regulatory mechanisms for the use of artificial intelligence in civil aviation will be created sooner than most sectors leading to scope and potential for guided development, deployment and use of artificial intelligence in the civil aviation sector.

Outcome Analysis This section is dedicated towards showcasing the deficiencies in the areas of international civil aviation governance with the help of existing unregulated advancements of artificial intelligence technologies. The illustration below shows the percentages of types of artificial intelligence technologies used in the space sector-

Fig. 5. Percentages of types of artificial intelligence technologies used in the space sector.

Therefore, in light of all the chapter sections above, it is apparent that the civil

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aviation sector has deeply and most prominently benefitted from machine learning and deep learning followed by various forms of neural networks such as dynamic, static, convolutional, deep and artificial neural networks. A balance between the remaining artificial intelligence technologies being used can be witnessed including the reliance on hardware as well as non- artificial intelligence fourth industrial revolution technologies. This chapter has also showcased that the involvement of the private sector has been much more greater than that of the public sector which has resulted in quicker deployment of artificial intelligence technologies that benefit not only airline companies and airports, but also benefit the end consumer. If compared to other sectors, the global civil aviation sector seems to have taken active initiative in order to consult the relevant stakeholders globally in order to create a well- regulated mechanism for the use of artificial intelligence technologies in the global civil aviation sector.

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International Law & Applied Sciences

Manohar Samal

Introduction Applied sciences refer to the body of knowledge or study which is mainly concerned with the use, application and implementation of demonstrated truths to solve practical issues and problems (Biology Online, 2019). There are several branches of applied sciences such as agronomy, agriculture, food science, forestry, horticulture, permaculture, architecture, computing technology, education, electronics, energy storage, engineering, environmental science, forensic science, health science, applied linguistics, mathematics, management, marketing, military science, microtechnology, applied physics and space science. However, for the purposes of the present chapter, only a few branches of applied sciences will be assessed depending upon the impact of artificial intelligence on these branches. In other words, all branches where the use of artificial intelligence has not been pivotal, will not be included in the present chapter. In pursuance of the above, only agriculture, education, health science, accounting, forensic science, architecture, marketing and military science will be examined since the deployment of artificial intelligence in these branches of applied sciences have been insurmountable and there is also a possibility of regulating such branches through international law.

Legal Background International law has been regulating various branches of applied sciences since time immemorial. This is evident from the fact that in agriculture, various international legal instruments such as the Convention on Biological Diversity 1992, Cartagena Protocol on Biosafety to the Convention on Biological Diversity 2000, Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits Arising From Their Utilization to the Convention on Biological Diversity 2011, Agreement on Agriculture 1995, World Trade Organisation Agreement on the Application of Sanitary and Phytosanitary Measures 1998 (SPS Agreement), Grains Trade Convention 1995, Food Aid Convention 1999, International Plant Protection Convention 1997 and the International Treaty on Plant Genetic Resources For Food and Agriculture 2009 already exist (Reteau, 2016). In the field of education, the United Nations Educational, Scientific and Cultural Organisation has played a

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pivotal role which has led to the signing and ratification of the Global Convention on the Recognition of Qualifications Concerning Higher Education 2019, Convention on Technical and Vocational Education 1989 and the Convention Against Discrimination in Education 1960 (United Nations Educational, Scientific and Cultural Organisation, 2020). In terms of health sciences, international legal instruments such as the Convention For the Protection of Human Rights and Dignity of the Human Being With Regard to the Application of Biology and Medicine 1997, Health Protection and Medical Care (Seafarers) Convention 1987, Agreement on the Establishment of the International Vaccine Institute 1997, Nursing Personnel Convention 1977, Occupational Cancer Convention 1974, Occupational Safety and Health Convention 1981, Radiation Protection Convention 1960, Agreement on the Transfer of Corpses 1973 and the WHO Framework Convention on Tobacco Control 2003. In terms of accounting standards, even though there are no multilateral legal instruments, there seem to be the International Financial Reporting Standards (IFRS Foundation, 2020) and the International Accounting Standards (Deloitte, 2020). There seems to be no international legal instruments for forensic science, architecture and marketing. The field of military science has seen insurmountable number of treaties and conventions under international law such as the Convention on the Prohibition of Military or Any Other Hostile Use of Environmental Modification Technique 1976, Comprehensive Nuclear- Test Ban Treaty 1996, Treaty Banning Nuclear Weapon Tests in the Atmosphere in Outer Space and Under Water 1963 (Partial Nuclear- Test Ban Treaty), Treaty on the NonProliferation of Nuclear Weapons 1970, Convention on the Prohibition of the Development, Production and Stockpiling of Bacteriological (Biological) and Toxin Weapons and on their Destruction 1975, Convention on Prohibitions or Restrictions on the Use of Certain Conventional Weapons Which May Be Deemed to Be Excessively Injurious or to Have Indiscriminate Effects 1980, Convention on the Prohibition of the Development, Production, Stockpiling and Use of Chemical Weapons and on their Destruction 1993 (Chemical Weapons Convention), International Instrument to Enable States to Identify and Trace, in a Timely and Reliable Manner, Illicit Small Arms and Light Weapons 2005, Arms Trade Treaty 2014, Convention on Cluster Munitions 2008, International Code of Conduct Against Ballistic Missile Proliferation 2003, Convention on the Prohibition of the Use, Stockpiling, Production and Transfer of Anti- Personnel Missiles and on their Destruction 1997, Treaty on Open Skies 2002 and the Treaty on the Prohibition of the Emplacement of Nuclear Weapons and Other Weapons of Mass Destruction on the Sea- Bed and on the Ocean Floor and in the Subsoil Thereof 1972.

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Conjunction with Artificial Intelligence The role of artificial intelligence in the development of agriculture, education, health science, accounting, forensic science, architecture, marketing and military science has been significant which has led to complete sectoral development of each of these applied science branches. Agriculture has always had a major contribution to the global economy and coupled with artificial intelligence, it is hoped that global hunger can also be eradicated (Talaviya et al., 2020). Recently, artificial intelligence has been deployed in the field of education as well and it has boosted the sector to newer heights (Kuprenko, 2020). The field of medical healthcare or health science has also benefited due to the use of artificial intelligence technologies such as neural networks, deep learning and natural language processing (Jiang et al., 2017). The use of artificial intelligence has also made accounting and auditing practices more convenient, less time consuming and cheaper for individuals and entities (Marr, 2020). The deployment of artificial intelligence in forensic science is still fairly new but has great potential and possibilities making the work of investigation and allied authorities easier (Gupta et al., 2020). Artificial intelligence technologies are also being deployed in architecture for designing, planning, parametric architecture and for creating smart buildings (Reddy, 2020). The use of artificial intelligence in the field of marketing is so high that a new sub- field known as artificial intelligence marketing has emanated and includes automated decisions made by artificially intelligent technologies for the purposes of marketing leading to the reduction of human effort (Marketing Evolution, 2020). One of the most traditional forms of application of artificial intelligence has been seen in the military and defence sector which has led to significant advancements, automation and nation capacity (Morgan et al., 2020).

Case Studies This section of the chapter is dedicated towards analyzing case studies of artificial intelligence applications on agriculture, education, health science, accounting, forensic science, architecture, marketing and military science which are branches of applied sciences that will help in establishing the intended results of making data and information on artificial intelligence applications in these branches compiled. Agriculture The field of agriculture has seen various usages of artificial intelligence technologies. A German based startup company called PEAT has introduced

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an artificially intelligent application called Plantix which uses deep learning, computer vision and image recognition to identify nutrient deficiencies in soil and also identifies pests and plant diseases which helps the farmers in making the decision about fertilisers (Pravar, 2020). Another company called Trace Genomics uses machine learning to help farmers with soil analysis and monitor the health of crops leading to increased agricultural productivity (Pravar, 2020). The company iUNU has launched its LUNA AI platform which assists in monitoring plant health (Drotleff, 2019). A company called SkySqurrel Technologies uses artificially intelligent drones to monitor crop health with the help of computer vision, machine learning, image recognition, object recognition and artificial neural network software embedded in the drones (Pravar, 2020). Artificial intelligence is also being combined with robotics and an ideal example of this is Blue River Technology’s artificially intelligent robot called See & Spray which uses computer vision, machine learning and artificial neural networks to precisely spray weeds on cotton plants reducing herbicide expenditure upto 90% (Faggella, 2020). A company called Harvest CROO Robotics has developed an artificially intelligent robot for harvesting crops and has been used for harvesting strawberries with the capability of harvesting upto 8 acres in a single day and replacing 30 human labourers (Faggella, 2020). Abundant Robotics has developed a similar artificial intelligence powered robot for picking apples (Malapela, 2017). A company called aWhere combines machine learning and big data analytics with satellite data in order to enhance crop sustainability through weather prediction (Faggella, 2020). The company Taranis is an artificial intelligence powered agricultural intelligence platform which has collaborated with companies making driverless tractors using computer vision, machine learning and deep neural networks to complete their tasks (Bandoim, 2019). CNH has also introduced its range of artificially intelligent self- driving tractors which have proved to have turned many heads (Hamblen, 2020). The company Mooo Farm is working in collaboration with Microsoft in order to use machine learning and computer vision for monitoring cattle health (Sarkar et al., 2020). The company Connecterra has also developed similar technology for monitoring cattle health and behaviour (Holcomb, 2020). An Australian company called Yield has introduced its robotic fish powered by artificial intelligence called Shoal Fish to improve decision making in aquaculture with the help of machine learning, swarm intelligence, internet of things and computer vision (Alltech, 2017). An Indian based company called Aquaconnect has also developed its artificially intelligent tool which helps aquaculture farmers to monitor farm operations in real time for a wide variety of factors such as optimized feeding, management, advisory and disease prediction (Mahalakshmi, 2020). Artificial intelligence has also led to the creation of several robotic milking robots such as the Astronaut A5 by Lely, the Voluntary

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Milking System by DeLaval and miRobot (Singh, 2019). The company Lely has also developed Grazeway System and Qwes which determines if the cattle can be sent out to graze and only allows the milking of those cattle which have grazed (Singh, 2019). Education One of the most prominent artificial intelligence technologies in the field of education are developed by Carnegie Learning and Content Technologies that provide learning, testing and feedback to students right from pre- kindergarten levels up to college levels (Marr, 2020). Arizona State University has been using Alexa for its routine campus needs since it helps out students with their schedules, dispatch of learning materials and the like (Kuprenko, 2020). Netex Learning has developed its own artificial intelligence platform to provide real time feedback, personalized cloud platform, virtual and digitalized learning and curriculum (Kuprenko, 2020). Few other artificially intelligent platforms offering similar services include DreamBox, Khan Academy and Achieve 3000 (Kuprenko, 2020). Third Space Learning has been developed by London University College and uses artificial intelligence in order to improve teaching techniques and one of its features is to notify the teacher if he or she is speaking too fast or slow (Kuprenko, 2020). The companies Little Dragon and CTI develop smart apps and smart learning possibilities with the help of artificial intelligence which use adaptive technology to adapt to the user’s emotions (Kuprenko, 2020). Similarly, ThinkerMath is an artificial intelligence solution which assists children in learning mathematics (Kuprenko, 2020). Microsoft Office Powerpoint artificially intelligent plugin called Presentation Translator develops subtitles in real time to help individuals with hearing disabilities to learn in real time (Rangaiah, 2020). Carnegie Mellon University has developed the iTalk2Learn System16 which uses artificial intelligence to understand the cognitive needs, emotional state and information about academic knowledge of the students (Faggella, 2019). The University of Southern California Institute for Creative Technologies is also combining the power of artificial intelligence, computer animation and 3D gaming in order to create a platform for providing education through virtual human- like characters (Faggella, 2019). The company Nuance has used artificial intelligence in its software with the help of natural language processing, speech and text recognition for student and faculty use (Schroer, 2018). The company Knewton combines adaptive learning with artificial intelligence to identify gaps in the student’s knowledge, suggest relevant coursework, get students on track for college level courses and also assists tutors (Schroer, 2018). The company Cognii uses artificial intelligence for corporate training

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and education purposes (Schroer, 2018). Other technologies boosting the education sector include artificially intelligent tools developed by Querium, Century Tech, Kidsense, Kidaptive, Blippar, Thinkster Math, Volley and Quizlet (Schroer, 2018). Health Science The healthcare and health sciences sector have significantly benefited from the use of artificial intelligence and few examples of its use and deployment include Path AI which has collaborated with Bristol- Myers Squibb and Bill & Melinda Gates Foundation in order to develop an artificially intelligent tool using machine learning, big data analytics and neural networks to assist pathologists in detecting cancers in a more effective way and in suggesting accurate treatments and Buoy Health which uses artificial intelligence to play the role of a symptoms and cure checker with the help of natural language processing, speech and text recognition, machine learning and artificial neural networks (Daley, 2019). The company Enlitic uses deep learning medical tools for streamlining radiology diagnosis, analyzing unstructured medical data and to assist doctors in dealing with real time needs of the patient (Daley, 2019). Companies like Freenome and Zebra Medical Vision are using artificial intelligence to detect cancers in the early stages itself and Beth Israel Deaconess Medical Center is using artificial intelligence to detect deadly blood diseases at extremely early stages (Daley, 2019). The use of artificial intelligence has also been extended to the pharmaceutical sector. The company Bioxcel Therapeutics uses the power of artificial intelligence to develop medicines in the field of immuno- oncology and neuroscience (Daley, 2019). Companies like Berg Health, Xtalpi and Atomwise harness the power of artificial intelligence for diagnosing, treating and developing new drugs for rare diseases (Daley, 2019). The company Deep Genomics has been using artificial intelligence in order to develop drugs and medicines for neuromuscular and neurodegenerative disorders (Daley, 2019). Benevolent AI has been using deep learning to ensure the right treatment for the right patient at the right time (Daley, 2019). The company Olive is using artificial intelligence to enable the healthcare industry to perform repetitive tasks automatically (Daley, 2019). Qventus is an artificially intelligent tool which helps hospitals in effective patient management, charts the fastest path for ambulances and solves operational challenges in emergency rooms (Daley, 2019). Babylon Health, CloudMedX, Cleveland Clinic in collaboration with IBM and Johns Hopkins Hospital use artificial intelligence for improving healthcare services to patients (Daley, 2019). Artificial intelligence technologies have also been utilised for mining, collection and management of medical data and notable examples include the

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technologies developed by Tempus, KenSci, Proscia, H2O.ai, Google Deepmind Health and ICarbonX (Daley, 2019). The combination of robotics with artificial intelligence has also led to fruitful results and the company Vicarious Surgical is one of the prominent examples. The company has developed its autonomous robot which uses artificial intelligence to perform invasive operations with the help of doctors controlling the robot with virtual reality (Daley, 2019). The company Auris Health has also developed its own artificially intelligent robots to assist surgeries and conduct diagnosis (Daley, 2019). The company Accuray has developed its CyberKnife System which uses artificially intelligent robotic arms to treat cancerous tumours all over the body (Daley, 2019). Other combinations of artificial intelligence and robotics in the field of health science and health care services include Intuitive’s Da Vinci Platform, Microsure and Mazor Robotics (Daley, 2019). Accounting and Document Review UK based Arria’s artificially intelligent software is one of the prominent examples of the use of artificial intelligence for accounting and is capable of analyzing complex accounting data with great ease due to machine learning and natural language processing capabilities (O’ Neill, 2016). KPMG has also been using McLaren Applied Technologies (MAT) which uses artificial intelligence in its audit processes (O’ Neill, 2016). KPMG has also partnered with IBM for using its supercomputer called Watson (O’ Neill, 2016). Similarly, even Deloitte has partnered with Kira Systems in order to implement their artificial intelligence powered tool for contract review and document management purposes (O’ Neill, 2016). A German based company called Smacc uses artificial intelligence to help small and medium sized companies to automate their accounting systems (Najjar, 2019). E&Y has also been using artificial intelligence in order to review lease contracts (Lin, 2019). PwC has collaborated with H2O.ai to create GL.ai which uses artificial intelligence to analyse documents and prepare reports (Lin, 2019). The company Botkeeper also assists accountants in fixing the errors and in not repeating the errors again and is also capable of learning through tracking with the help of machine learning and neural networks (Botkeeper, 2020). Forensic Science One of the most prominent usages of artificial intelligence in forensic science is being developed by Monash University in collaboration with the Victorian Institute of Forensic Medicine and will significantly contribute to ballistics investigation (Umali, 2019). One of the earliest forms of artificial intelligence

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used in the field of forensic science is MADIK which functions on a rule based expert system (Mitchell, 2010). Researchers from the University of Leon in collaboration with the Spanish National Cybersecurity Institute have developed an artificial intelligence system which is almost capable of performing the tasks of a full- time investigator and does a wide range of study of data obtained from the crime scene, reconstructing a face, reconstructing dead bodies and has also successfully managed to assist in apprehending officials involved in a human trafficking case in Thailand (Baraniuk, 2019). Architecture In the field of architecture, use of artificial intelligence technologies has changed the way in which designing and implementation takes place. Researchers at the Massachusetts Institute of Technology (MIT) have been developing artificially intelligent drones which interact with each other in order to construct small architectural models (Alexander, 2020). The company 3XN has been conducting a research project on artificial intelligence which has displayed results on the usefulness of artificial intelligence through research, organisation of information and designing in architecture (Cutieru, 2020). A London based startup company called AI Build has collaborated with ARUP Engineers to create Daedalus Pavilion with the help of artificially intelligent robots to showcase the capabilities of artificially intelligent built structures (Cutieru, 2020). The company called Sidewalks Labs has developed its artificially intelligent tool which is capable of creating urban planning scenarios (Cutieru, 2020). The use of artificial intelligence has not only been restricted to software and tools but has also been witnessed in architectural installations. The perfect example of this is the architectural pavilion project installation called Ada which uses artificial intelligence to create a performative environment on the basis of the visitors’ facial expressions (Cutieru, 2020). Graphisoft has developed its artificially intelligent software called EcoDesigner Star which allows architects and planners to build and conduct an environmental analysis without leaving their computers (Longread, 2016). The company Space Syntax has developed its own artificial intelligence software called DepthMapX which uses artificial intelligence to study the spatial network of a city for design processes (Wood, 2017). Another artificially intelligent tool using similar technology is Unity 3D (Wood, 2017). The company called Autodesk has introduced its artificially intelligent software called BIM 360 IQ which helps in risk assessment of architectural projects (Bharadwaj, 2019). Smartvid.io has launched its artificially intelligent platform called Very Intelligent Neural Network for Insight & Evaluation (VINNIE) which uses deep learning, natural language processing, speech and text recognition and deep neural networks to increase safety while implementing architectural

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designs in construction (Bharadwaj, 2019). The company Autodesk has also created an artificial intelligence powered platform called Project Dream Catcher which is capable of generating thousands of potential designs (Roberts, 2020). A Berlin based startup company called CoPlannery uses artificial intelligence for design and planning purposes (Roberts, 2020). Marketing Use of artificial intelligence in the field of marketing has also generated fruitful results. Albert is an artificial intelligence enabled marketing platform which indulges in autonomous media buying, autonomous optimisation and management of paid ad campaigns (Karlson, 2017). Slackbot is another artificially intelligent tool which manages Google Analytics data in order to ensure better management of websites and deal with anomalies (Karlson, 2017). Rocco is another artificially intelligent marketing tool which suggests fresh social media content for marketing purposes (Karlson, 2017). A company called Vidora has developed its artificial intelligence powered tool which improves churn prediction for marketing purposes (Karlson, 2017). Companies like Pinterest, Amazon, Google Photos and Facebook have been using image recognition artificial intelligence technologies in order to identify people and objects to improve targeted advertisement and for other marketing improvement strategies (Karlson, 2017). Chase Bank has collaborated with Persado to use artificial intelligence for improving their marketing (Moreno, 2019). Starbucks has also been using artificial intelligence and predictive analysis in order to serve personalized recommendations for their customers (Moreno, 2019). The multinational oil and gas company called Shell has also introduced its artificial intelligence powered platform called Shelly to boost marketing activities (Savidge, 2020). The British Bank HSBC had partnered with Maritz Motivation Solutions to take feedback and preferences from their customers with the help of artificial intelligence which also helped them in better decision making on improving their services (Savidge, 2020). AmazonGo is Amazon’s artificial intelligence enabled platform which conducts customer preference analytics in order to suggest better models and solutions for improving services and increasing profits (Savidge, 2020). Even Uber has used and continues to use artificial intelligence for its pricing strategy as per data collected on customers which has boosted its business activities (Savidge, 2020). Phrasee is another artificial intelligence enabled tool which indulges in autonomous social media management (Kaput, 2020). Other notable artificially intelligent marketing tools include Acquisio Turing, Automat, CaliberMind, Drift, Emarsys, Jetlore, Salesforce Einstein and ZetaHub (Odden, 2020).

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Military Science As reiterated above, one of the oldest applications of artificial intelligence has been seen in military sciences. The United States of America has implemented various artificial intelligence programs and few notable ones are Project Maven, OFFSET Program, Defense Advanced Research Projects’ Agency (DARPA) and the SquadX Experimentation Program (Goled, 2020). China has also been improving its Blowfish A2 model with the help of artificial intelligence technologies being developed by Military- Civil Fusion National Defense Peak Technologies Laboratory in collaboration with Tsinghua University and Ziyan UAV (Goled, 2020). Even India has introduced the High Level Defence AI Council to invest and take up defence and military related projects with the use of artificial intelligence (Goled, 2020). The Central Intelligence Agency (CIA) of the United States of America has 140 projects in motion which aims to use artificial intelligence for various national security and defense related activities (Congressional Research Service, 2020). DARPA is also using artificial intelligence in one of its programs called Target Recognition and Adaptation in Contested Environments (TRACE) to identify and locate targets (Singh and Gulhane, 2018). The US Army and Navy have also introduced various programmes which use artificial intelligence for training and warfare analysis in partnership with companies like CACI, SAIC, Millennium Engineering and Torch Technologies (Singh and Gulhane, 2018). The Indian Defense Research and Development Organisation has its own artificial intelligence laboratory called Centre for Artificial Intelligence and Robotics which has invested to create a Multi Agent Robotics Framework which will be an array of robots that will be included in the Indian Army (Nagpal, 2020). DARPA’s Advanced Targeting and Lethality Automated System (ATLAS) uses artificial intelligence to give ground-based combat vehicles autonomous target capabilities (Gronlund, 2019). Russia is building a new city called Era which will be completely devoted to military innovation with the help of artificial intelligence (Gronlund, 2019). The UK Government has awarded a contract to Blue Bear Systems to create an artificially intelligent swarm of drones which will assist in defence and national security related operations (Gronlund, 2019). The French company Dassault is working on the nEUROn project which involves the development of an artificial intelligence powered autonomous unmanned combat air system (Gronlund, 2019). The Israel Aerospace Industries has been developing autonomous artificial intelligence powered weapons since many years and one of its notable inventions is Harpy Loitering Munition which is an autonomous and unmanned aerial vehicle capable of bombing ground targets with explosive warheads (Gronlund, 2019).

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South Korea has been developing an artificially intelligent fleet of unmanned military units called Dronebot Jeontudan (Gronlund, 2019). The South Korean Government has already invented sentry robots which use artificial intelligence and it is called the SGR- A1 Sentry Robot that has been successfully deployed in the demilitarised zone separating North Korea from South Korea (Gronlund, 2019). DARPA has also launched the Air Combat Evolution program which will explore artificial intelligence technologies in order to improve autonomous air combat technology (Rana, 2020). A company called Thales has invented RAPIDFire which is an artificial intelligence enabled autonomous weapons turret (Roth, 2019). Rafael Advanced Systems have introduced the GIL 2 Handheld Rocket System which uses artificial intelligence enabled computer vision to enhance targeting (Roth, 2019). L3 Technologies’ HE- 4G Missile also uses similar technology for effective targeting (Roth, 2019). DARPA and Lockheed Martin have collaborated to create Behavioral Learning for Adaptive Electronic Warfare System which uses artificial intelligence to attack and disable wireless communication networks and is also capable of neutralising improvised explosive devices (IEDs) (Roth, 2019). The company Orbital Insights has introduced its artificial intelligence enabled software for assisting defence activities and operations with the help of satellite imagery (Roth, 2019). Various Government departments in the United States of America such as the Central Intelligence Agency, Federal Bureau of Investigation and the Los Angeles Police Department have been using Palantir and Stabilitas that harness the power of artificial intelligence and predictive analytics for the purposes of maintaining homeland security (Roth, 2019). Lockheed Martin and NGRAIN (Canada) Corporation have developed artificial intelligence technologies for the effective maintenance of military aircraft which reduce costs and provide real time damage and repair data including pilot safety (Roth, 2019). Lockheed Martin has also introduced the Convoy Active Safety Technology System (CAST) which is used on military trucks for the purpose of forming a convoy with the help of computer vision and artificial neural networks (Roth, 2019).

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Section 5: AI & Economics

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International Labour Law

Sanad Arora and Mayank Narang

Introduction Artificial Intelligence is growing at an exponential pace and has become the topic of discussion of many academics around the world. However, while discussing the applications of AI it is also equally pertinent that we make it a point to discuss its impact on the labour market. While AI is termed to be a “disruptive technology” much of the disruption which it will cause can be countered by thorough discussion and policy making. It has always been the rule that policy follows technological change, but the regulation and policy around new technology takes a lot of time to develop which often leads to exploitation of the common folk until a law is put into place. This kind of strategy would prove to be archaic when it comes to AI because unlike those other technologies Artificial Intelligence would remain far from static and would develop at an exponential pace. Hence it becomes all the more important that we adopt an extremely expeditious approach towards policy making when it comes to AI. Even though much debate and discussion with relation to how AI will eradicate jobs that are menial and require less application of the mind does exist but the other qualitative applications of AI such as the creation of jobs in different fields or the rise of a surveillance state in the working environment because of the different ways AI will be used to keep a check on employees is not getting the attention which it needs. A plethora of devices are being worked upon by IT companies which will track worker performance and invade their right to privacy just to ensure that they are being efficient for the company (Limitless worker surveillance, 2017 p. 101). This entire idea of “management by algorithm” even though good for the company may spell doom for its employees owing to the boundless capabilities of AI such as analyzing the time taken to finish a task via making the employees wearing trackers, scanning the emails and texts of employees etc. all this data when collected would allow the company to execute decisions in relation to workforce management just on the basis of insights given to them by the AI It is important that instead of asking the question whether these technological innovations are good or bad but rather the questions we should be asking is that what all changes will be brought forth by these new technologies that would affect the world and how can we regulate such affects to an optimal level so that they do not end up having any detrimental effects for the society. It is usually the case that the technologies which are developed in relation to

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software have a veil surrounding them and more often than not, regulatory organisations are not able to get their hands on the source code of such software which makes them unable to conduct an audit of the algorithm of such software’s are using i.e. to find out whether these algorithms lead to any biased or discriminatory results or not. Essentially AI and automation are separate principles but in today’s time when AI is being used in machines to carry out tasks which are both intellectually demanding and simple repetitive tasks. To deal the with the beast we refer to as AI, we need to adopt a handson approach, the entities which are affected by AI have to spur the social dialogue and unionize in order to effectively bargain with the corporates as to how they are going to use AI to analyses the data which it is collecting and also negotiate the kind of data which is being collected so that their privacy is not violated. There has been a trend of increased spending in the sector of AI and it is not just the corporates, but the governments of several nations are taking the initiative to developing an AI empowered economy (Introduction: Automation, Artificial Intelligence, and Labour Protection, 2019). This shift will lead to a tremendous change in the job market where the only thing to worry about would not just be the job loss because of the automation but also the quality of jobs that are being generated by this technological shift. Many academics have argued that the jobs that will be created in the era of AI will further increase the inequality within the working population (Introduction: Automation, Artificial Intelligence, and Labour Protection, 2019). The job loss because of automation wouldn’t be the only factor that is resulting in increase in inequality in the society, but other factors such as relocation of production units which manufacture technology-based equipment and the option of crowd employment available to the companies would also contribute to rising inequality amongst the society. Hence when it comes to policy making it is best not to tunnel vision ourselves into just focusing on automation. Due to the surge in access of the internet and connectivity in developing countries, crowd employment has become an easy source for companies in developed countries to hire professional labour on contractual basis and leaving them bereft of any worker benefits. Crowd employment also known as crowd work, is the outsourcing of work online to individuals or groups in exchange for payment (Eurofound). In such kind of outsourcing a larger more complex task is divided into smaller tasks which can be standardized and so that it can be distributed amongst groups of people who will specifically only tackle that task (Eurofound). Even though these jobs pay high remunerations, however such work is provided to people on contractual basis leaving them bereft any worker privileges, which they otherwise would have had access to if they were formally employed at the company. Such evolved methods of employment coupled with algorithmic management casts doubt on the common assumption of occupation through digital platforms having positive outcomes. Even though

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such platforms make work available to people in developing and offer them good salaries, it can be inferred such “contractual workers” offer the companies more profitable solutions because through these methods they can evade minimum wage and employment benefit requirements which are the norm in their country. As of now it can be concluded that there are much wider social, legal and policy related issues which need to be tackled with well-reasoned perspectives at both domestic and international level. Considering that it is just now that we are seeing extensive implications of automation and AI, through this chapter the authors hope to provide the readers with an in-depth analysis of the dialogue around labour law and AI while making an attempt to address the pertinent topics which need to be kept in mind before setting up a regulatory framework for AI with regards to labour law.

Legal Background Why does ascertaining the impact of AI on future workforce matters. In the earlier days, the biggest threat to the working class or the labour class have faced has either been the relaxation of labour laws to allow for more investment or the outsourcing of manufacturing jobs to a country like China. But in today’s era all these threats seem little in comparison to the threat of automation. With the advancements in AI and Automation it is being predicted by many analysts that it is both the routine and some advanced jobs that are at risk of being devoured by AI (Estlund, 2017). Throughout history there has been this tendency that after every industrial revolution there has been a shift of manpower to a different sector for example from agriculture and artisan shop to clerking and manufacturing, to management and service occupations (Oxfordmartin.ox.ac.uk, 2017). People in the past have also been against automation but in the long run, the automation has also cheapened production making the final price of the products fall, thus increasing the real income of the people which increases demand for other goods, thus essentially absorbing the job loss which was a result of automation. But the industrial revolution which will be a consequence of AI and automation is different, unfortunately will only bring with its job opportunities for the highly skilled individuals who work in the technology sector and will lead to hollowing out of middle-income jobs. The reason the reallocation of labour was possible because we were always able to find new uses for labour was because labour was always able to adapt itself to the changing environment with the help of education but now that computerization is becoming cognitive, eventually it will be able to teach itself things at stupendous speeds which humans will not be able to compete with. Recent research on the topic also suggests that there has been a decline in the

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demand in of skilled workers but despite of that the supply of workers with higher education has continued to grow (Economic Policy Institute). Another important finding from the research was that the high skilled workforce has been pushed down the occupational ladder which has resulted in a domino effect and pushed the lower skilled workforces further down the ladder or even out of the workforce in some cases. This notion of mass unemployment has again triggered the age-old notions of universal basic income and heavy government investment in the development of human capital (Murray, et al., 2013). The topic of automation and job losses has been up for major debates where many academics have argued while placing their reliance on historical events that every industrial revolution has been able to absorb job losses but as mentioned above this industrial revolution is different where even left-wing economists argue that the wage stagnation problems still is a consequence of weakening labour unions and globalization of trade and finance but Harvard economist Richard Freeman begs to differ. He has argued that the technological shift has had a huge impact on the distribution of income and has made capital/automation intensive industries more attractive than labour intensive industries (Freeman, 2015 pp. 1, 9). As robots become more and more capable for performing more complex tasks, individuals and businesses who have the necessary capital to invest in them will invest and substitute them for human labour, because they will not only be cheaper than employing human labour they will also save on the workplace benefits which they would have provided to their human workforce in normal course. This would compound the problem of declining wages and lessen the bargaining power of the human workforce which would be extremely detrimental for them. To further elucidate this observation an example can be found in relation to the ‘Google Translate’. In 2016, Jason Furman, then Chairman of the White House Council of Economic Advisors, touted language translation to be a skill which can only be mastered by humans and used it as an anchor to argue about human’s supremacy over the machine’s (Obama White House, 2016). But just a few months later Google released an updated version of the Google Translator, which was drastic improvements over the previous version, because of AI and machine learning (Lazzaro, 2017). It got so good that it could match the ability of a human translator and the results of the words which were translated were almost instantaneous. In a research conducted by the McKinsey Global Institute, they have conducted a very detailed comparative analysis between the abilities of humans and machines to perform tasks, so it can figure out what percentage of tasks can be automated in today’s economy (McKinsey & Company, 2017). The MGI researchers identified eighteen distinct human capabilities in five broad categories—sensory perception, cognitive skills, natural-language processing, social and emotional skills, and physical skills— and analysed how current technology compares against human performance on

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these parameters (McKinsey & Company, 2017). It reached the conclusion that humans still manage to outperform machines when it comes to tasks that involve sensing others emotional state or responding to emotions but that is where the advantage ends, tasks which involved the comparison of any other parameter were performed better by computers (McKinsey & Company, 2017). They divided the jobs into 7 categories and ranked the jobs from least to most risk of being automated: management and development of people (9%); application of expertise to decision making, planning, and creative tasks (18%); interacting with stakeholders like customers, suppliers, or the public (20%); and unpredictable physical activities (26%). Much more automatable are collection of data (64%); processing of data (69%); and predictable physical activities (81%). After conducting a more specific analysis the research also provided for industry wise predictions, in the accommodation and food services sector is 73% automatable; work in the health care and social assistance sector is just 36% automatable. Even some low wage which involve unpredictable jobs are hard to automate such as janitors, landscape workers, and domestic workers, or social and emotional skills like childcare or eldercare workers. According to the study it might take two to six decades to apply large scale automation, the only variable being the speed of development of technology. McKinsey in this research identified “labour costs” as the most determinable factor when it comes to deciding whether the management will move towards automation or not. If the cost of operating, acquiring and maintaining the technology is lesser than the salaries they are paying to the humans which are employed at their company or business, they will shift towards automation. Taking into consideration the recent trends and the heavy inclination of the companies towards automation, it becomes extremely pertinent that we study the effects of AI and automation on the labour class and tackle this issue with appropriate policy and regulation, before such kind of technological developments result in an unemployment pandemic where only the people belonging to the rich and affluent classes of the society would be able to appropriate off automation or attain the necessary the education to get employed deepening the already massive economic and class divide in the society. How to develop an approach to tackle automation. While deliberating and finding an answer to the problem of automation it is important that we also take into account other issues which are plaguing the labour market to arrive at a more holistic and effective response for automation: • Outsourcing of work to places which have low manufacturing/labour costs • Declining Labour Standards • Existing Legal Framework

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Fissuring is the act of migration of jobs or outsourcing of jobs to contractors who perform those tasks for a cost that is lower than what the business was paying for it when it was integrated within the business. When automation and fissuring are compared it can be inferred that the simple reason for resorting to either of those practices depends whether they reduce the overall cost of operations without sacrificing the efficiency of the organization or not. Fissuring is not just bad for the workers that were employed at the business, it is no bed of roses for the replacement workers who are employed at the outsourcing companies either. Because of the tough competition and nonadherence to labour standards, their companies are compelled to cut costs in whatever way they can. This results in erosion of labour standards and decline in wages and also allows for companies to save money through illegal means by not complying with regulatory practices. Reasons behind fissuring and automation: Heightened competition, Technology, Cost of Labour. There are two kinds of fissuring which policy makers need to be worried about: outsourcing jobs to countries where the manufacturing of the product is more cost effective or the labour for the service rendered is cheap and the conversion of actual jobs into gigs and the hiring of employees on a contractual basis. Outsourcing of work to different countries allows the businesses to benefit from cheaper labour costs benefit from, weaker regulatory mechanisms and depressed union standards. When it comes to disintegration of jobs into gigs, Uber serves as the prime example for it. It hires it drivers on contractual basis and does it burden itself with the employment relationship. The legal entitlements and benefits of employees which are promised to them on conventional basis are this way not just watered down but completely gotten rid of making the cost of operations really marginal, because of which the Uber is able to provide their customers with cheap services at the cost of labour exploitation. This transition from full time employment to leaner supplier firms because of excessive competition has led to a lot out of downward pressure on the value of labour. Businesses which are unable to find cheaper means of production in the heat of heightened competition risk falling prey to their more efficient competition, this has been the age-old rule of capitalism since its very foundation. With technology accelerating the integration of the world into a global economy aided by the legislative framework to foster international business networks and supply chains, to make up for efficient movement for goods, services and capital, has further stimulated the firms to opt for fissuring only if it generates higher profits or reduced production costs. Now for the very same reasons because of which people are opting for fissuring where the firms have to manage complex supply chains, invest a lot in time in logistics and quality inspection, they might choose automation over fissuring if they can supply those inputs more quickly, more reliably, more cheaply, or with less risk,

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then lead firms will turn to them instead of human labour. The third factor which might persuade businesses to opt for fissuring or automation is the cost of employing human labour. This all comes down to cutting costs with relation to health care, insurance, disability benefits, contingent lawsuits etc and even the human managers which you need to hire to make sure that the human workforce is working efficiently. A human employee gets sick, takes vacation, might even indulge into unscrupulous practices and sell important company data, needs to be insured, is also susceptible to negligent behaviour often resulting in liabilities against the firm. All such factors come into the mind of the employer when deciding between hiring a human and automation and if automation through more technological developments eventually becomes affordable enough, it would become the go to choose of each and every employer. A basic postulate of labor economics holds that increases in the cost of labour whether due to market forces, legal mandates, or collective bargaining tend to lead firms to substitute capital, including technology, for labour (Reed, 1987 pp. 174-75). The policy and regulatory approach to deal with automation will have to acknowledge the existing legal framework for labour and the disadvantages which burden it in comparison to automation, as discussed earlier one of them is the worker benefits and labour standards which the employers have to adhere to adding to the costs of the business. This legal framework which is there to protect the workers is the cause of businesses resorting to automation and outsourcing. There are some legal provisions which directly add to the cost of labour such as payroll taxes for workers’ compensation and unemployment insurance, Social Security, and Medicare, which can add 18 to 26% to the base salary cost (Pagliery, 2013). Many academics have devised that outsourcing of work to other countries can be stopped by creating laws which would prohibit businesses to do so (David, 2014). But such actions will actually be counterproductive and would actually end up harming the labour class because this would just push the businesses towards automation and would force them to substitute human labour for machine. In addition to these direct costs which are added to the cost of employment, the employment of human labour also entails some indirect costs which can arise under different laws, the most appropriate example of this being litigation. There are many laws which regulate the behaviour of employees in a workplace environment and any dispute under those laws adds to both monetary and reputational loss for the firms those employees are working in. An employee or an ex-employee might file a suit against another employee for misconduct. Human Employees through their lax behaviour towards work might create liabilities for the businesses they are working in under tax, securities, consumer protection or environmental laws due to lack of compliance on their part. Because of this firms have to invest in a lot of money into legal compliance

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professionals so that they could help them in avoiding or managing these liabilities. It is a very straight jacketed observation that employment laws add to the overall cost of operations because of which both corporate law and financial-market pressures virtually compel firms to minimize these costs if doing so increases returns to shareholders and such costs can be avoided partially through fissuring and completely through automation (Hansmann, et al., 2004 p. 33). Even though a parallel can be drawn between automation and fissuring when it comes the comes to the reasons as to why the business would opt one or the other, instead of employing a human workforce for their business, automation has a big advantage over fissuring when it comes minimizing the cost of the business. Automation results in a complete substitution of the human workforce and provides the employer with a absolute free pass from bearing the costs, risks, and difficulties of employing people, including those that arise from the law of work unlike fissuring which only provides for a partial solution. Such a trend can be noticed in the fissuring haven of the world which is China, where after the rise of minimum wages some countries have shifted their suppliers to other countries which provide cheaper labour prospects because of which Chinese Factories are being forced to automate themselves (The Economist, 2015). Unlike human labor, machines tend inexorably to get more capable and cheaper over time (Ford, 2015). With the development of technology resulting in cheaper costing software’s and robots, and ever so increasing cognitive abilities of AI because of continuous cloud-based machine learning, they will outpace human skill and cost effectiveness in no time. The organizational innovations that come under the rubric of fissuring, though aided by technology, still have to rely on human performance and while also managing the cost of sustaining human beings and reproducing their labour. Automation overcomes both of these requirements. It offers firms the ultimate exit from bearing the cost of human employment and also eliminates all the indirect costs which the employer had to bear in the form of reduction of workers’ exposure to occupational illness or injury, discrimination, retaliation, and excessive hours. Because of such obvious advantages of automation and fissuring over conventional employment, there is a major predicament which exists when developing a response to those two issues. Many scholars have argued on prohibitions against fissuring or automation and creation of a legal fortress of employment, expanding the rights and duties which are to be borne by the employer and extend such duties to the labour which the firm is employing via means of fissuring. But such a solution would again increase the cost of employment and lead employers towards automation. Hence it can be concluded that any proposal to expand the responsibility of the employer would drive them away from employing humans. If any kind of response is developed to fix the conundrum of fissuring, automation comes in and thwarts

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any attempt at increasing employer responsibility via policy measures. This by no means proves the intention of lawyers, human rights activists or other stakeholders wrong. The problem of fissuring and automation if not regulated would also be detrimental for the economy where it would result in “premature de-industrialization” which means that economies of developing countries would experience declining employment in the industry before attaining the income, resources, and infrastructure needed to advance to a post-industrial economy (Rodrik, 2015) and cause an economic turmoil.

Conjunction with Artificial Intelligence Dehumanization of labour. The robot Sophia made by Human Robotics made its debut to the world in 2016, Sophia has cameras in her eyes and is capable of maintaining eye contact (Raymundo, 2016). She can also maintain conversations with human beings and has given interviews before (Insider.com, 2017). Sophia was also awarded citizenship by Saudi Arabia which became a matter of major debate amongst the scholars (Vincent, 2017). Even the parliament of EU has deliberated on the matter of giving robots “electronic personality” “creating a specific legal status for robots, so that at least the most sophisticated autonomous robots could be established as having the status of electronic persons with specific rights and obligations” and to apply this electronic personality “to cases where robots make smart autonomous decisions or otherwise interact with third parties independently” (Delvaux, 2016). The report certainly does not draw any parallels between humans and smart robots and neither does it equate smart robots with humans but it discusses the concept of giving them an “electronic identity” in relation to how we associate the concept of “legal identity” with companies. Legal personality has proved vital for economic development, by allowing people to keep their personal assets separate from the assets of a corporation and, therefore, fostering investments in business initiatives including trade and manufacturing (Deakin, et al., 2005). Despite the economic advantages which were provided by a separate legal identity, it also had its disadvantages where this concept was used to evade liability especially in the cases of cases of taxes and social responsibility. The acknowledging of legal rights of non-human beings can be a cumbersome process, it might have its advantages, but it can be easily used by the owners of the robot to avoid liability and leave other people who interact with the robot at risk of receiving no damages whatsoever in case any harm is caused to them by the robot. There is a vast variety of implications in diverse matters which need to be discussed such as intellectual property, data protection and respect of human rights. Corporations despite of having a separate legal identity do not get associated as

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human beings because they don’t exist in a physical space and are intangible, even though their names can be associated with their products, whereas when it comes to robots will exist in the space as human beings and even though many people might argue that giving robots legal identity and the capability of having rights won’t equate them with humans but consciously or subconsciously might have certain implicit or unintended consequences. It has been recorded in a study that humans feel alienated when they are made to work with machines (Eurofound, 2018). When people realize that a robot which is essentially a non-living being has the same rights and legal capacity as that of a human being, they not only feel dejected but this drawing of parallels between a robot which is doing a task which was initially performed by a human and is doing it with greater efficiency leads to dehumanization of human labour and also stimulates its commodification (Vardaro, 1986 pp. 75-140). Dehumanization occurs when an individual views another person in negative ways, which leads to the belief that the other person is undeserving of the respect and kindness usually afforded to oneself and another person (O'Neal, 1994). The use of robotics or other IT tools which work alongside humans runs the risk of dehumanizing their work because it can just be viewed as an extension of the robot or the IT tool itself. This has very bad impact on the perception of labour being performed by humans and the work which they perform is not treated with the same dignity as any other work which humans perform and is also depreciated in valued. Algorithmic Discrimination. With the advancements in technology surveillance has scaled new heights nowadays. Data of employees in being collected through wearables which use AI and machine learning to process that data. Workers at the warehouses of the company Amazon are aided by certain wearable devices which guide them to the item they have to process, such tools also enable for monitoring of their employees and tracking their efficiency (Baraniuk, 2015). GPS systems are being used to track position of truck drivers and also maintain an all time check at the drivers as to how much time they take to cover distances. This technology has gone as far giving insight into whether such drivers have a common meeting spot or not because through this information the company can make sure that the workers are not unionizing and subdue any action on their part before it even begins (Stefano, 2016). Any dips in efficiency or rise in holidays can lead to the dismissal of the workers (Commoditized workers: Case study research on labour law issues arising from a set of ‘on-demand/gig economy, 2016). The workers who freelance on platformed based services are at the behest of the reviews of their employer if they want to get hired next after their gig is complete (Foundation for European Progressive Studies). Metadata about the employees can be collected from email, time stamps, duration of meetings (Humanzye). The wearable devices which are used by companies can be highly

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intrusive and violative of privacy. In some of the cases companies are using wearable devices with embedded microphones to track the modulation of their employees’ voice to assess whether they are in a good mood or not (Fischbach, et al., 2009). Because such wearable devices are given to employees by companies, they are usually not made aware of the extent of the data which is being collected. The work which is being done of the computers by the employees is also being made a target of surveillance by companies like Crossover where they sell a worker monitoring system through which the work they are performing on their computers can be monitored and a screenshot of their computer is taken and a time card is made every ten minutes. This timecard is then shared with the workers and their managers via a “logbook where all of your timecards are displayed and a dashboard summarizes your timecards to show you how you spent your time” (Crossover). Companies are also using software like Interguard that record and reports on data such as web history and bandwidth utilization “whether the employee is on or off network” (Interguard). Investigating of an employee’s social media, giving them wearables, which can even track their off-work routines all amount massive invasion of privacy which can be or are being done by companies. Personal data gathered on the Internet, also by acceding to information available through social networks is also increasingly used to make hiring decision, and the practice of asking employees to disclose their social network passwords is also spreading, so that 18 individual states of the United States passed legislation explicitly banning it. Sometimes collection of such data can be justified when it comes to ensuring security levels and promoting harmony in the company. Wearables that analyse fitness data, for instance, can be employed to mitigate health and safety risks, including stress, and to prevent accidents. The advent of AI enabled surveillance devices has made it very easy for companies to amass a huge amount of information on their employees in both professional workspaces and intimate spaces. Such over the top forms of surveillance can also lead to increased worker stress and decreased productivity (Moore, et al., 2018). Because the working of these systems is unknown to the individual who is being made subject to such technologies, you can never know whether the said system is affected by any explicit or implicit biases or not. For example, the software might be too productivity focused and might exclude people with disabilities from employment. Thus, it can be said that management by algorithm because it lacks sensitivities which are akin to human minds might not lead to desirable outcome in workplaces. Because the machines and algorithms are working with very narrow notions of efficiency and productivity, they fail to take into account into account the numerous hidden costs associated with schedule instability and produce suboptimal results for the business (Williams, et al., 2018).

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Planning the future of labour regulation. The labour class has always been handed the short end of the stick when it comes to their struggle of raising labour standards, expanding employee rights and benefits, and improving the enforcement of legally guaranteed worker entitlements. Even though the laws and regulatory which are present are to benefit the worker and ensure equity in the society, they are often scene as vices to the economy by private companies because they add to the costs of the business and reduce profits, reaching to the same conclusion that employment costs will push people towards automation. The decline in employment in the future because of automation will only be acceptable for an economy, if they are developed and resourceful enough to fairly distribute leisure, income, and the work that remains which is an extremely difficult standard to achieve. The challenge before us is to make employment of humans more attractive while also maintaining labour standards. One of the ways to tackle this predicament can be to shift the burden of worker benefits from the employer to the employee, the productivity linked benefits such as health insurance and provision for periodic training and skill development programmes can be handled by the employers but other benefits like leave travel allowance can be shifted to be borne by the employees themselves. Such transition of benefits might provide employers with the incentive to avoid outsourcing their work to offshore contractors or replacing their labour workforce with machines. Even though the popular thought about labour regulation is that they stifle economic growth but much evidence to the contrary exists where it has been shown that how labour laws play an important role in preventing unfair competition and encouraging firms to organize their production more efficiently. By not being allowed to undertake low-road strategies, enterprises are compelled to pursue other approaches, besides cheap labour, in their pursuit of profit (Contractual status, worker well-being and economic development, 2017). Even the Fair Labor Standards Act of 1938, which introduced a federal minimum wage explained that, “Fair Labor Standards Act was not a straitjacket or a device to harass employers…[rather] they found that its moderate minimum standards were in fact contributing to the improvement of working conditions, to the elimination of unfair competition and to increased purchasing power in their communities’’ (Tyson, 1950 pp. 278-286). Having a strong labour institution have been associated with good performance in maintaining employment levels stable during the recent financial crisis in Germany (Bohachova, et al., 2011). Making lifelong training of employees is mandatory in some way or form by the employer and the government so that they are better prepared to adjust to new machinery can also help in reduction of employment. It is important workers organisations make aware the government of practices like management by algorithm and other predatory

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practices which are followed by corporates so that the quest for higher productivity does not result in occupational hazards and heightened stress for the workers involved. As mentioned before, because AI might lack the cultural and societal sensitivities of a human being they might provide arbitrary ways to increase efficiency which might not be feasible at all or be extremely strenuous for the workers to comply. Such decisions should be over seen by a human and any such practices should be subject to governmental regulation. Instead of adopting the black box approach the company applying AI in their work environment should be mandated to keep such practices transparent and open for governmental and public scrutiny. “Human-in-command”, an approach advocated by the European Economic and Social Committee’s Opinion on Artificial Intelligence, namely the “precondition that the development of AI be responsible, safe and useful, where machines remain machines and people retain control over these machines at all times” and “workers must be involved in developing these kinds of complementary AI systems, to ensure that the systems are useable and that the worker still has sufficient autonomy and control (human-in-command), fulfilment and job satisfaction” should be strictly followed also concerning work. It is important that we strengthen our labour forces to battle this inevitable wave of automation not just to protect them but also to prevent widespread economic fallouts like de-industrialization, which might lead to the elimination of a substantial number of middle-income jobs, exponentially increasing the income gap between the rich and the poor, making our world a socially skewed world to live in.

Case Studies McKinsey Global Institute estimates that almost one half of all activities which humans are engaged in could be automated. But what could and cannot be automated is more of a hypothetical question because a lot of factors such as the cost of automation, social acceptance, quantity of labour etc would need to be ascertained before we can get into that debate. Well one thing that can be ascertained is that the jobs which are repetitive in nature or require a lower level of skill of to perform would be the ones most affected by automation. The Washington based Brookings Institute has reported that middle wage jobs have reduced the most owing to the substitution of humans by automation and the main reason which has been cited for such automation has been the repetitive character of such jobs (Muro, et al., 2019). In accordance with the research conducted by the institution it was also found out that it is the lower educated workforce which will be affected the most. The changes which are brought in by AI and automation have two sides to them, on one hand it will have an adverse impact on the lower educated workforce but on the other hand it will

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create opportunities for many who are willing to adapt to the change in the work environment as a consequence of this revolution. Human Resources AI has and will play an important role when it comes to workforce management. One of the areas where this impact of AI can be seen to be most significant will be the management of Human Resources. Humans are prone to make errors and have explicit or implicit biases when it comes to making subjective decisions selecting employees is one of them. When it comes to HR many problems occur in relation to interviewer biases, errors, skewed questionnaires, conformity to governmental regulations etc. Pymetrics has highlighted in its research that humans perform poorly when it comes to analyzing prospective employees for the company, it has stated how 50% of the decisions taken are subject to biases and essentially lead to a lack of efficiency for the company15. Jessica Miller-Merrel, who is a globally recognized HR consultant has written on the implications of AI use in HR wherein she has mentioned that AI can be used in candidate screening, candidate engagement, candidate re-engagement, post-offer acceptance, new hire onboarding, career development, employee relations, HR compliance and case management, and scheduling16. Marketing AI has come forward to be as an essential tool for marketing, with the immense computing power at its behest, AI can do a statistical analyses of huge amounts data such as the surveys filled out by customers, the purchase patterns of customers etc. and come out with unique insights which would not have otherwise been possible. Advertising is an integral part of marketing; it is essentially used to sway potential customers to buy the product. AI has already transformed the way advertising works, of the most prominent examples being Google AdWords. The AI which is used in Google AdWords asks the individual who wants to advertise three questions which are: 1) which product or service he wants to advertise 2) what the budget for advertising is 3) who the target client for their product or service is. Website design is another which has become child’s play with the onset of AI, companies such as Wix, Grid, Responsive Grid etc. which use AI and machine learning to make websites for their clients just on the basis of the information they have gathered by asking 15 16

For a discussion, see Josh Constine’s work (Constine, 2017). A summary of the ways may be found in a number of websites. Some of the categories stated herein are from Jessica Miller-Merrell’s work (Miller-Merrell, 2016).

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them a few questions. Content Curation and Content Creation being the other two fields which fall under the ambit of marketing where the use of AI has become very ordinary. Content creation companies like Wordsmith are language generation models which are able to replicate human generated where it is able to transform data into useful narratives. When it comes to Content Curation, companies like Netflix, YouTube, Amazon etc. have suggestions columns where the AI estimates what the consumer of their service would prefer to watch next based on the content they have watched previously. Search engines also have similar AI embedded in them, where they recommend the user things that they would like to surf on the internet based on their previous searches and this also such search engines then advertise products to those individuals. Search Engines Accomplished search engines like Google have employed AI to take the precision and utility of their searching capabilities one step further where it is even able to provide accurate results through the use of AI called Rank Brain (Search Engine Land),even when the words which are put into the search engine are misspelled. The AI uses machine learning to take the words and the phrases that are typed in search queries and associates to the ones that are similar in nature, to increase the relevance of the search results. It is essentially a software which uses the past data which it has gathered while also factoring in the behaviour of the user who is making those searches and with the use of machine learning automatically makes adjustments to those searches (Modassic Marketing). Law Firms The world has gone through many technological revolutions before and legal profession has always seemed to remain unaffected from the developments around it. In the face of exponential development, the legal world has stuck to its same old archaic methods with very minimal change. Whatever technological change that has taken place, has been to improve the efficiency and management of office related tasks. But the fourth technological revolution which brings with it the dawn of artificial intelligence, is disruptive to an extent beyond our possible imagination and has the potential to shake the very core of the functioning of the legal world and its actors. When it comes to the legal industry, AI is beginning to encroach upon jobs which even though repetitive in nature require a lot man power and some interpretation skills to. As of now AI is mostly offering solutions to lawyers when it comes to document reviewing

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and fact checking tasks. JP Morgan has been working on its own in-house AI technologies, one of the biproducts of that research which particularly shines from the rest is called COIN (Contract Intelligence) Contract intelligence was able to interpret commercial loan agreements, which amounted to 360,000 hours of lawyer’s time annually (Bloomberg, 2017). A mergers and acquisitions (M&A) lawyer founded a company called Kira Systems; the software prowess achieved by this company helps M&A lawyers extract business information from thousands of pages of M&A contracts (Kira Systems). Another important area in the legal field which humans can’t perform well is the task of due diligence. Usually lawyers working late hours, find it very monotonous to verify documents for any mistakes, but these mistakes if not caught can change the entire intention behind the document. Upcoming companies like Ebrevia with the help of AI extract important contract clauses and help lawyers in reviewing and analyzing them (eBrevia). When it comes to the area of research one company that come out to be particularly efficient is Ross Intelligence as its services provide lawyers with recommended case laws and secondary readings after searching through mounds of data, severely cutting down the time required for research (ROSS Intelligence). Another field where the use of AI is showing some promise is in the field of prediction, for example Intraspexion, Lex Machina and Ravel are some of the few companies which use analytics and machine learning to predict the outcome of the case (Fagella). With more of these technologies flooding the market, law firms can surely be expected to make a cut back in hiring adversely affecting employment. Accounting The “Big 4” accounting firms KPMG, Ernst and Young, Deloitte and Price Waterhouse Cooper are indulging into the application of AI too, a simple reason for this can be the large amounts data which they have to analyse. On the basis of the data analysis which these firms will acquire with the help of AI they will be able to notice certain patterns and correlation in the data which might not have been possible if they only relied on the capability of humans. In a dog-eat-dog world, the only entities who survive are the ones who keep up with the changing tide. In 2015 article Deloitte categorizes AI technologies into three categories which are (Deloitte): Product: Where AI is embedded into the product itself Process: AI to smoothen workflow and reduce either the manpower or the time to do tasks Insight: Cognitive Technologies which use machine learning

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The auditors at Deloitte are accessing AI tools armed with natural language processing are being used to read, review and interpret the documents which are provided by their clients such as contracts, leases and other deeds. Deloitte has also partnered with IBM Watson to provide the customers with AI enhanced technology reducing the cost and increasing the efficiency of tasks. For example, Deloitte in their partnership with IBM Watson has developed this software called “leasepoint” where the software would help companies and other business entities meet compliance standards (Deloitte). As the name suggests “leasepoint” is a software which is built for the real estate market with the help of other software’s like IBM TRIRIGA it aims to have a smart approach towards real estate management and making more tactical leasing decisions with the help of analytics (Deloitte). KPMG has announced that it will be spending 5 billion dollars in AI, it is mainly concerned towards building cloud-based technologies and focusing on AI and data analytics that could be utilized for both audit processes (Chawla, 2020). Ernst and Young have also started using AI based technology to review and audit documents which is saving their employees a lot of time. They had a test run for their AI tech in 2019 and the results were phenomenal with 90% reduction in time and a 25% increase in accuracy (Chawla, 2020). The use of AI in accordance with the categorization provided by Deloitte most of the AI which is being used by these accounting firms can be said to fall in the realm of “Process” oriented applications. As can be inferred from the above-mentioned applications AI will serve as a dominant factor for the future of accounting services. Even though the use of AI will raise accounting standards and increase efficiency and decrease cost of operations in the long but such investment into proprietary AI technology which might create disparity in the market. But as of now due to accountants not being well equipped in using AI oriented technologies, they won’t be able to make use of it to its utmost potential. The use of AI by the ‘Big 4’ does mark a change in the way of accounting practices and sooner or later majority firms would be inclined towards using AI because of its benefits, which is why educational institutions should start training students to use AI, so that they can prepare themselves for the future and adapt to changes which are taking place within their profession.

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International Sea Law

Manohar Samal

Introduction Earth’s water resources consist of various components such as oceans, seas, rivers, freshwater sources, polar ice caps and groundwater (USGS, 2020). Oceans and seas comprise 96.54% of Earth’s water resources followed by polar ice caps, glaciers and permanent snow which is at a meagre 1.74%. This is followed by groundwater at 1.69%, freshwater sources at 0.76% and rivers at 0.0002% (USGS, 2020). The remaining water sources are not covered since the law of seas does not concern itself with them. Artificial intelligence has transformed the interaction of human beings with Earth’s water resources. This is evident from the fact that artificial intelligence technologies are being deployed for advancing research at oceans, seas, glaciers and polar ice caps, conducting shipping operations, salvage operations, exploitation of natural resources present in water resources, fishing, constructing artificial islands and other structures, use in coastal areas and preservation, cleaning operations, marine resource, polar ice cap and glacier conservation activities. Due to such wide adoption, it is necessary that the law of seas under international law can encompass the right legal tools which will be capable of governing the usage and outcomes of artificial intelligence on Earth’s water resources. Therefore, the present chapter is aimed at identifying the artificial intelligence technologies being used on water resources and in assessing if the current international law of sea is capable of regulating the usage and outcomes of such technology.

Legal Introduction The law of sea consists of rules which govern the use of sea including all allied resources and its adjoining environment (University of Melbourne, 2021). In general, the law of sea governs the freedoms, obligations and rights in the area of territorial seas, high seas, shipping, fishing, wreckage, conservation and dispute settlement (University of Melbourne, 2021). Traces of the law of the seas can be principally found under public international law and aspects surrounding law of seas under public international law have been discussed below.

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Public International Law Customary International Law. The law of the sea is one of the oldest branches of public international law (Tufts University, 2021). One of the earliest customary international law principles in the law of seas was the principle of freedom in the high seas which prevented States from claiming territory over high seas or from preventing other States from equally exercising their right of utilising the resources of the high seas (Tufts University, 2021). Another commonly known and widely accepted international customary law principle is the right of innocent passage (Tufts University, 2021). This principle permits foreign vessels to pass over the territorial waters of another State peacefully either without entering the internal waters, or from the internal waters but without stopping provided that all the procedures of the State have been followed (Tufts University, 2021). The principle of equidistance is another customary international law of sea principle (Talmon, 2015) and it states that a nation State’s maritime boundary should stick to a median line which is equidistant from the shores of neighbouring nation States (Dhanpal, 2016). Another principle emanating as a subset of the principle of freedom of high seas is the principle of freedom of maritime communication (Talmon, 2015). The principle of territorial sovereignty is also a valid customary international law of sea principle because it advocates for absolute sovereignty in territorial waters (Dellapenna, 2001). The principle of historical waters is also relevant here since it is a principle used by nation States to claim territory by proving historical use (Dellapenna, 2001). It is relevant and noteworthy that most of these customary international law principles on the law of seas have been encompassed in international legal instruments related to maritime international law.

International Legal Instruments. The most prominent international legal instrument governing the law of the sea is the United Nations Convention on the Law of the Sea 1982. Other international legal instruments include the Global Programme of Action for the Protection of the Marine Environment from Land- Based Activities (United Nations, 2021), International Convention for the Safety of Life at Sea 1974, International Convention for the Prevention of Pollution from Ships 1978, International Convention on Standards of Training, Certification and Watchkeeping for Seafarers 1978, Convention on the International Regulations for Preventing Collisions at Sea 1972, Convention on Facilitation of International Maritime Traffic 1965, International Convention on Load Lines 1966, International Convention on Maritime Search and Rescue 1979,

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International Convention for Safe Containers 1972, Convention for the Suppression of Unlawful Acts Against the Safety of Maritime Navigation 1988, Protocol for the Suppression of Unlawful Acts Against the Safety of Fixed Platforms Located on the Continental Shelf, Convention on the International Maritime Satellite Organisation 1976, Torremolinos International Convention for the Safety of Fishing Vessels 1997, 1993 Torremolinos Protocol, Special Trade Passenger Ships Agreement 1971, Protocol on Space Requirements for Special Trade Passenger Ships 1973, International Code for Ships Operating in Polar Waters 2017, International Convention Relating to Intervention on the High Seas in Cases of Oil Pollution Casualties 1969, Convention on the Prevention of Marine Pollution by Dumping of Wastes and Other Matters 1972, London Protocol 1996, International Convention on Oil Pollution Preparedness, Response and Co- operation 1990, Protocol on Preparedness, Response and Co- operation to Pollution Incidents by Hazardous and Noxious Substances 2000, International Convention on the Control of Harming AntiFouling Systems on Ships 2001, International Convention for the Control and Management of Ships’ Ballast Water and Sediments 2004, Hong Kong International Convention for the Safe and Environmentally Sound Recycling of Ships 2009, International Convention on Civil Liability for Oil Pollution Damage 1969, 1992 Protocol to the International Convention on the Establishment of an International Fund for Compensation for Oil Pollution Damage, Convention Relating to Civil Liability in the Field of Maritime Carriage of Nuclear Material 1971, Athens Convention Relating to the Carriage of Passengers and their Luggage by Sea 1974, Convention on Limitation of Liability for Maritime Claims 1976, International Convention on Liability and Compensation for Damage in Connection with the Carriage of Hazardous and Noxious Substances by Sea 1996, International Convention on Civil Liability for Bunker Oil Pollution Damage 2001, Nairobi International Convention on the Removal of Wrecks 2007, International Convention on Tonnage Measurement of Ships 1969, Convention on the International Maritime Organisation and International Convention on Salvage 1989.

Conjunction with Artificial Intelligence The use of artificial intelligence has significantly transformed various activities related to interaction of human beings with the Earth’s water resources. These include a wide array of activities such as research and exploration at oceans, seas and polar ice caps, shipping operations, salvage operations, exploitation of mineral and other natural resources, fishing and its allied activities, construction of artificial islands, construction of structures on oceans and seas, coastal applications of artificial intelligence, cleaning

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operations at oceans and seas, preservation of marine resources and preservation of polar ice caps. These applications of artificial intelligence technologies have been discussed in detail in the case studies and the multidisciplinary analysis sections of this chapter.

Case Studies This section of the chapter is dedicated towards analyzing case studies of artificial intelligence applications in research and exploration at oceans, seas and polar ice caps, shipping operations, salvage operations, exploitation of mineral and other natural resources, fishing and its allied activities, construction of artificial islands, construction of structures on oceans and seas, coastal applications of artificial intelligence, cleaning operations at oceans and seas, preservation of marine resources and preservation of polar ice caps which will help in deriving the intended results. Research and Exploration at Oceans, Seas and Polar Ice Caps Artificial intelligence technologies have been successfully used in research and exploration of oceans, seas and polar ice caps. The United Kingdom Government had successfully deployed its national autonomous underwater vehicle (AUV) called Autosub6000 which collected more than 150,000 images in a single dive beneath the ocean surface of North East Atlantic Ocean using artificial intelligence (University of Plymouth, 2019). Google’s Tensorflow has also been used for assessing data collected beneath oceans and seas with the help of convolutional neural networks (University of Plymouth, 2019). Researchers at North Carolina State University have developed its own artificial intelligence tool to study the history of oceans and seas (Chin, 2019). The Woods Hole Oceanographic Institution has developed an artificially intelligent autonomous underwater vehicle called Nereus for conducting research and exploration activities in oceans and seas and by far, is the deepest diving underwater vehicle available (Theo, 2019). The Stanford Robotics Lab has also developed its own remotely operated artificially intelligent vehicle called OceanOne ROV for ocean and sea exploration activities (Theo, 2019). Similar such autonomous robot has been developed by the Schmidt Ocean Institute as well and it is called the SuBastian (Schmidt Ocean Institute, 2018). Similar such technology is also being developed by Gazprom in collaboration with Yandex. Even the Hindustan Petroleum Corporation Ltd. is using artificial intelligence in order to conduct research and manage data collected from such research in oceans and seas (Theo, 2019). Crabster is an artificially intelligent underwater robot developed by the Korea Institute of Ocean Science and Technology for

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scientific exploration activities (Theo, 2019). The Government of the United Kingdom has funded a project by Systems Engineering & Assessment Ltd. and University Institute for Mathematical Innovation to create an artificially intelligent tool to map the surface of the ocean floor in order to enhance effective research activities (Just, 2020). IBM is working on an artificially intelligent autonomous microscope which will help in assessing the health of planktons in their natural marine environment (Pedersen, 2019). The Danish Meteorological Institute, Technical University of Denmark and Harnvig Arctic & Maritime have teamed up for a project called the Automated Sea Ice Products that uses artificial intelligence technologies to collect timely information about sea- ice information (European Space Agency, 2019). The BAS AI Lab has developed its own artificial intelligence tool to understand Arctic sea loss and to improve the tracking of iceberg populations in the Southern Ocean (British Antarctic Survey, 2020). A Professor at the Memorial University of Newfoundland has been using NVIDIA Tesla GPU Accelerators with the CUDA parallel computing platform to train convolutional neural networks for effectively determining how climate change is affecting sea ice (Beckett, 2017). Shipping Operations and Salvage The use of artificial intelligence has been humongous in shipping operations. The company Arundo has developed its own artificially intelligent software which helps in performance forecasting of shipping operations (DeChant, 2021). Hitachi Europe Ltd. has partnered with Stena Line to use artificial intelligence in order to reduce fuel consumption costs and contribute to their social responsibility for protecting the environment (DeChant, 2021). Orient Overseas Container Line has teamed up with Microsoft in order to create an artificially intelligent tool which helps with auto- switching and auto- scaling throughout its business with the help of cloud computing and artificial intelligence (DeChant, 2021). Similar such tools are also being used by Wartsila. A United States based company called Maana has developed artificial intelligence powered software to boost decision making by better management and use of data (Thetius, 2021). In fact, this technology has been deployed by Shell and Chevron as well (Thetius, 2021). The company Sea Machine Robotics has built its artificially intelligent system which helps in autonomous control and remote command on commercial vessels (Thetius, 2021). The company Sea Machines has collaborated with Maersk to install an artificially intelligent situational awareness technology for their ice container vehicles (Thetius, 2021). The company Marine Digital has introduced its own artificial intelligence software for collecting data from the vessels’ sensors and external sources such as satellites and weather stations to improve decision making and fuel

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optimisation during shipping operations (Marine Digital, 2021). The company Kalmar is using artificial intelligence for fuel optimisation during shipping operations (Martin, 2019). Mitsui O.S.K. Lines Ltd. has been using the power of artificial intelligence and big data analytics in order to calculate costs of operations (Martin, 2019). A shipping company called Shone has been using artificial intelligence for robotizing its ships to automatically detect hazards around the ship to avoid accidents (Martin, 2019). Rolls Royce has also been using an artificial intelligence enabled voyage system which is capable of steering vessels from hazardous objects and can also automatically dock on ports (Martin, 2019). Archaeologists from the University of Patras have used artificial intelligence to discover a 2,000-year-old Roman ship called Fiskardo by analyzing images (Merkusheva, 2020). Sea- Kit is an autonomous and unmanned vessel which harnessed the power of artificial intelligence for recovering other large automatic underwater vehicles and for rapid seabed visualisation (Merkusheva, 2020). Odyssey Marine Exploration has been using robotic divers and sonar technology coupled with artificial intelligence which has resulted in the discovery of hundreds of shipwrecks (Sheridan, 2013). Exploitation of Resources, Fishing and Allied Activities Artificial intelligence technologies are being used for monitoring the orderly exploitation of natural and mineral resources present underwater and also for fishing and allied activities. Companies like ExxonMobil have been working with MIT in order to develop artificially intelligent robots for oil and gas exploration in oceans (Theo, 2019). A Canadian company called Nautilus Minerals is developing its own artificially intelligent robots for conducting mineral mining activities (Hebert, 2017). Global Fishing Watch has built an artificial intelligence tool which increases transparency for fishing vessels (Pedersen, 2019). Even Satlink is using artificial intelligence to collect data and apprehend fishing vessels which overexploit fishing activities (Pedersen, 2019). A company called Observe Technologies has developed its own artificial intelligence tool which processes data in order to provide information to aquaculture farmers so that they can make the right decision as to what and when to feed fish (Beijnen and Yan, 2020). Another company called eFishery has developed an artificially intelligent tool that uses the power of artificial intelligence and sensors to determine hunger levels in fish and shrimps (Beijnen and Yan, 2020). The company Umitron Cell uses similar technology (Beijnen and Yan, 2020). There are various artificial intelligence use cases for preventing and tracking diseases during aquaculture as well and these include Aquacloud,

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FarmMOJO, SHOAL and XpertSea (Beijnen and Yan, 2020). SINTEF Ocean is leading a project called the Smartfish H2020 which aims to develop artificial intelligence technologies for the purpose of accurately assessing fish stocks and ensuring compliance with fishery regulations in the European Union (Wooden, 2020). Nippon Steel and NEC are developing an artificial intelligence technology together in order to improve the study of fish environments (Synced, 2019). Sasebo Kokai Sokki, Sasebo City and Nagasaki Perfecture are developing an artificially intelligent tool which will help fishermen to adjust their fish catch in a manner to prevent the falling of prices due to overfishing (Synced, 2019). Artificial Islands and Other Coastal Applications Researchers and scientists from China have been working on a project called Deep Sea Atlantis which is creating the world’s first artificial intelligence enabled artificial island colony on the South China Sea (Theo, 2019). The artificially intelligent colony is planned to have a space station and robotic submarines (Theo, 2019). Saildrone Inc. is developing artificially intelligent unmanned surface vehicles to improve coast guard activities through threat detection at sea (Lee, 2020). PMAT is developing its artificial intelligence technology called X- CAP for improving coast guards’ decision making (Lee, 2020). A company called Cronj has also developed artificial intelligence tools for the Saudi Arabia Coast Guard that will help in automatically detecting intrusion and improve decision making (ANI, 2020). Cleaning Operations, Preservation of Marine Resources and Polar Ice Caps Artificial intelligence technologies are being used for cleaning operations, preservation of marine resources and preservation of ice caps. One prominent example of this is the artificial intelligence autonomous surface ocean robot created by Liquid Robotics and the National Oceanic and Atmospheric Administration (Hebert, 2017). The Government of Germany, Government of Netherlands, Government of Croatia, Government of France and Government of Romania have partnered to form the SeaClear System which will create artificially intelligent autonomous robots for cleaning up litter in oceans and seas (International Shipping News, 2020). The Plastic Tide Project uses artificial intelligence and drones to trace plastic waste in water bodies all across the United Kingdom (Hooijdonk, 2019). A non- profit organisation called Ocean Cleanup has partnered with Ikig.ai in order to detect plastic waste in oceans with the help of artificial intelligence technologies (Koleva, 2019). The German

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Research Centre for Artificial Intelligence have developed artificially intelligent drones to monitor pollution at oceans and seas which helps authorities to make better decisions and improves their tracking capabilities (Asendorpf, 2020). The International Union for Conservation of Nature, Global Change Institute and Underwater Earth in partnership with Google has developed XL Catlin Seaview Survey which uses artificial intelligence to monitor and reveal changes underwater in oceans and seas (Euceda, 2018). SphereCam is an artificially intelligent camera developed by the University of California, San Diego in order to monitor and track endangered marine species (Euceda, 2018).

Multi-Disciplinary Analysis This section has been dedicated towards analyzing the various technologies at play for artificial intelligence hardware and software being utilized for human interaction with the Earth’s water resources and its convergence with the law of the seas.

Name of Develope Artificial r Intelligence Technology or Project

Artificial Supporti Intelligence ng Software Hardware Type and Assisting Systems

Autosub6 000

Robotics, Autonom Machine ous Vehicle Learning and Artificial Neural Networks

Research and Exploration at Oceans, Seas and Polar Ice Caps

Convolutio nal Neural Network

Research and Exploration at Oceans, Seas and Polar Ice Caps

Tensorflo w

United Kingdom Government

Google

Purpose

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Unnamed Technology

North Carolina State University

Machine Learning and Artificial Neural Networks

Nereus

Woods Hole Oceanograp hic Institution

Machine Autonom Learning, ous Vehicle Computer Vision, Image and Object Recognition and Artificial Neural Network

Research and Exploration at Oceans, Seas and Polar Ice Caps

OceanOn e ROV

Stanford Robotics Lab

Machine Autonom Learning, ous Vehicle Computer Vision, Image and Object Recognition and Artificial Neural Network

Research and Exploration at Oceans, Seas and Polar Ice Caps

Unnamed Technology

ExxonMo bil and MIT

Machine Autonom Learning, ous Vehicle Computer Vision, Image and Object Recognition and Artificial Neural Network

Exploitati on of Resources, Fishing and Allied Activities

Unnamed Technology

Gazprom and Yandex

Machine Learning, Computer

Research and Exploration

Autonom ous Vehicle

Research and Exploration at Oceans, Seas and Polar Ice Caps

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Vision, Image and Object Recognition and Artificial Neural Network Unnamed Technology

Crabster

Hindustan Machine Petroleum Learning and Corporation Big Data Limited Analytics

Korea Institute of Ocean Science and Technology

Deep Sea China Atlantis Government Project

Unnamed Technology

at Oceans, Seas and Polar Ice Caps

Research and Exploration at Oceans, Seas and Polar Ice Caps

Machine Autonom Learning, ous Vehicle Computer Vision, Image and Object Recognition and Artificial Neural Network

Research and Exploration at Oceans, Seas and Polar Ice Caps

Deep Learning, Deep Neural Networks, Internet of Things and Convolutiona l Neural Network

Artificial Islands, Constructio n and Other Coastal Application s

United Artificial Kingdom Neural Government Network , Systems Engineering &

Artificial Island, Space Station and Robotic Submarine

Research and Exploration at Oceans, Seas and

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Assessment Ltd. and University Institute for Mathematica l Innovation Unnamed Technology

IBM

Unnamed Technology

Polar Caps

Convolutio Autonom nal Neural ous Network and Microscope Reinforceme nt Learning

Research and Exploration at Oceans, Seas and Polar Ice Caps

Global Fishing Watch

Artificial Neural Network and Machine Learning

Exploitati on of Resources, Fishing and Allied Activities

Satlink

Artificial Neural Network and Machine Learning

Exploitati on of Resources, Fishing and Allied Activities

Satellite

Research and Exploration at Oceans, Seas and Polar Ice Caps

Automate Danish Convolutio d Sea Ice Meteorologi nal Neural Products cal Institute, Network Technical University of Denmark and Harnvig Arctic & Maritime SuBastian

Ice

Schmidt Ocean

Convolutio Autonom nal Neural ous Vessel

Research and

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Institute

Network, Deep Learning, Computer Vision, Image and Object Recognition and Machine Learning

Exploration at Oceans, Seas and Polar Ice Caps

Unnamed Technology

Nautilus Minerals

Computer Mining Vision, Image Robots and Object Recognition, Robotics and Artificial Neural Network and Deep Learning

Exploitati on of Resources, Fishing and Allied Activities

Unnamed Technology

BAS Lab

Convolutio nal Neural Network

Research and Exploration at Oceans, Seas and Polar Ice Caps

GeoSURF Liquid Wave Glider Robotics and National Oceanic and Atmospheric Administrati on Unnamed Technology

Machine Wave Cleaning Learning and Glider Operations, Artificial Robot and Preservation Neural Sensors of Marine Network Resources and Polar Ice Caps

Memorial Convolutio University of nal Neural Newfoundla Network nd, NVIDIA

Research and Exploration at Oceans,

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Tesla GPU Accelerators and CUDA Parallel Computing Platform Unnamed Technology

Arundo

Seas Polar Caps

and Ice

Machine Learning and Deep Learning

Shipping Operations and Salvage

Unnamed Technology

Hitachi Machine Europe Ltd. Learning and and Stena Artificial Line Neural Network

Shipping Operations and Salvage

Unnamed Technology

Orient Cloud Overseas Computing Container and Machine Line and Learning Microsoft

Shipping Operations and Salvage

Unnamed Technology

Wartsila

Cloud Computing, Blockchain and Machine Learning

Shipping Operations and Salvage

Unnamed Technology

Maana

Artificial Neural Network and Machine Learning

Shipping Operations and Salvage

Unnamed Technology

Sea Machines Robotics

Artificial Neural Network and Robotics

Shipping Operations and Salvage

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Unnamed Technology

Sea Artificial Machines Neural Robotics and Network and Maersk LiDAR

Shipping Operations and Salvage

Marine Digital FOS

Marine Digital

Internet of Things and Machine Learning

Sensors

Shipping Operations and Salvage

Unnamed Technology

Kalmar

Machine Learning

Shipping Operations and Salvage

Computer Vision, Internet of Things, Image and Object Recognition and Machine Learning

Sensors

Shipping Operations and Salvage

Artificial Neural Network, Computer Vision, Image and Object Recognition and Internet of Things

Sensors

Shipping Operations and Salvage

Unnamed Technology

Mitsui Big Data O.S.K. Lines Analytics and Ltd. Machine Learning Shone

Rolls Royce

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Unnamed Technology

University of Patras

Sea- Kit

Unnamed Technology

Image Recognition and Machine Learning

Shipping Operations and Salvage

Artificial Autonom Neural ous Network, Unmanned Machine Vehicle Learning, Image and Object Recognition

Shipping Operations and Salvage

Odyssey Marine Exploration

Machine Learning and Artificial Neural Network

Shipping Operations and Salvage

Unnamed Technology

Observe Technologie s

Machine Learning and Artificial Neural Network

Exploitati on of Resources, Fishing and Other Allied Activities

Unnamed Technology

eFishery

Machine Learning and Internet of Things

Sensors

Exploitati on of Resources, Fishing and Other Allied Activities

Unnamed Technology

Umitron Cell

Machine Learning and Internet of Things

Sensors

Exploitati on of Resources, Fishing and Other Allied Activities

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AquaClou d Platform

FarmMOJ O

Seafood Innovation Cluster

Machine Learning and Cloud Computing

Aquaconn Machine ect Learning

Exploitati on of Resources, Fishing and Other Allied Activities

Unnamed Technology

SHOAL

Machine Robotic Learning, Fish Artificial Neural Network and Robotics

Exploitati on of Resources, Fishing and Other Allied Activities

Unnamed Technology

Xpert Sea

Machine Learning and Computer Vision

Exploitati on of Resources, Fishing and Other Allied Activities

Smartfish H2020 Project

SINTEF Ocean

Computer Vision, Big Data Analytics, Machine Learning and Artificial Neural Network

Exploitati on of Resources, Fishing and Other Allied Activities

Unnamed Technology

Nippon Machine Steel and Learning and NEC Artificial

Exploitati on of Resources,

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Neural Network

Fishing and Other Allied Activities

Unnamed Technology

Sasebo Kokai Sokki, Sasebo City and Nagasaki Perfecture

Machine Learning and Artificial Neural Network

Exploitati on of Resources, Fishing and Other Allied Activities

X- CAP

PMAT

Machine Learning and Artificial Neural Network

Artificial Islands and Other Coastal Application s

Unnamed Technology

Saildrone Inc.

Unnamed Technology

CronJ

Machine Unmanne Learning, d Artificial Autonomou Neural s Vehicle Network, Computer Vision, Image and Object Recognition

Artificial Islands and Other Coastal Application s

Machine Learning, Internet of Things, Image and Object Recognition, Computer Vision and Artificial Neural Networks

Artificial Islands and Other Coastal Application s

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SeaClear System

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Governm ent of Romania, Government of France, Government of Netherlands, Government of Germany and Government of Croatia

Deep Autonom Learning, ous Robots Computer Vision, Image and Object Recognition and Convolutiona l Neural Network.

Cleaning Operations, Preservation of Marine Resources and Polar Ice Caps

The The Machine Plastic Tide Plastic Tide Learning, Project Project Artificial Neural Network, Computer Vision, Image and Object Recognition

Drones

Cleaning Operations, Preservation of Marine Resources and Polar Ice Caps

Unnamed Technology

Ocean Machine Cleanup and Learning and Ikig.ai Image Detection

Cleaning Operations, Preservation of Marine Resources and Polar Ice Caps

Unnamed Technology

German Research Center for Artificial Intelligence

Drones

Cleaning Operations, Preservation of Marine Resources and Polar Ice Caps

Machine Learning, Computer Vision, Image and Object Recognition and Artificial Neural Network

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SphereCa m

University Computer of California, Vision, Image San Diego and Object Recognition

XL Catlin Internatio Seaview nal Union Survey for Conservatio n of Nature, Global Change Institute, Underwater Earth and Google

Computer Vision, Machine Learning and Artificial Neural Network

Camera

Cleaning Operations, Preservation of Marine Resources and Polar Ice Caps

The table above shows that artificial intelligence technologies used in human interaction with Earth’s water resources include convolutional neural networks, deep neural networks, artificial neural networks, machine learning, deep learning, computer vision, image and object recognition and the like, often combined with supporting hardware and allied non- artificial intelligence technologies such as internet of things, big data analysis, cloud computing and robotics. It is noteworthy that extremely less artificial intelligence technologies exist for supporting coast guards. Furthermore, the usage of artificial intelligence technologies in constructing structures at oceans and seas and constructing artificial islands is next to none. As far as the regulation of artificial intelligence through law of the sea or other allied international law instruments is concerned, the number of non- binding declarations and multilateral efforts are close to none, let alone binding instruments.

Outcome Analysis This section is dedicated towards showcasing the deficiencies in the areas of international law of the sea with the help of existing unregulated advancements of artificial intelligence technologies. The illustration below shows the percentages of types of artificial intelligence technologies used for human

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interaction with the Earth’s water resources-

Fig. 6. The percentages of types of artificial intelligence technologies used for human interaction with the Earth’s water resources

Therefore, in light of all the chapter sections above, it is apparent that Earth’s water resources have deeply and most prominently benefitted from machine learning, deep learning and reinforcement learning followed by neural networks and the remaining technologies and that reliance on hardware is wellbalanced. However, the chapter sections have also showcased that no specific regulations for water resources specific artificial intelligence technology exist in the international sphere under the law of seas. Moreover, the fact that most developers are either private sector companies or research organisations and labs from Universities and that, Government affiliated developers are less in comparison, the need for sufficient multilateral, bilateral and regional legal instruments for capacity building, investment incentivisation, increased and cross- border development done keeping in mind relevant national standards and issues revolving around artificial intelligence technologies is currently the need of these times.

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Section 6: AI & Governance

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International Humanitarian & Refugee Law

Aditi Sharma

Introduction Humanitarian milieus are characterized by complex social systems that are difficult to predict and grounded in culture, psychology, and deep knowledge bases. Therefore, international humanitarian law aims to protect in two ways: (i) people those who are not, or no longer, taking part in warfare; and (ii) by restricting on the means of warfare – in particular weapons and the methods of warfare, such as military tactics. Keeping this in mind, the first Geneva Convention was drafted in 1864 to protect people in armed conflicts and establish Red Cross emblem (now recognized as the International Committee for the Red Cross and Red Crescent, ICRC). Later, other emblems were recognized in order to protect treatment of civilians, prisoners of war and soldiers who are otherwise rendered hors de combat or incapable to fight. (The Rome Conference on an International Criminal Court: The Negotiating Process, 1999) In 2000, this convention was signed and ratified by 194 states, making it universally acceptable. Currently, there are four conventions and three protocols additional to the conventions.

Legal Background The First Geneva Convention for the Amelioration of the Condition of the Wounded and Sick in Armed Forces in the Field protects soldiers that are hors de combat or incapable to fight. It includes soldiers that are wounded and sick, military chaplains, wounded and sick civilians that support and accompany armed forces, medical personnel and civilians that take up arms to repel invasions. The Second Geneva Convention for the Amelioration of the Condition of Wounded, Sick and Shipwrecked Members of Armed Forces at Sea reflects the first convention but at sea. It applies to wounded and sick combatants on board ships or at sea, hospital ships and medical personnel, members that are shipwrecked, and civilians accompanying armed forces. The Third Geneva Convention Relative to the Treatment of Prisoners of War sets out regulations for treatment of prisoners of war. The articles in this convention focuses and require that the prisoners of war are treated humanely, they are

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provided with housing, food and proper medical care. They lay down guidelines on proper criminal trials, forbidden labor, disciple, and recreation. The Fourth Geneva Convention Relative to the Protection of Civilian Persons in Time of War ensures that civilians in the state at war must be protected at all costs. It lays down that civilians must be protected from cruel treatment, torture, brutality or any form of discrimination. The Common Article 3 to all four Conventions extends a coverage to conflicts that are not international in nature. It reads as: “1. In the case of armed conflict not of an international character occurring in the territory of one of the High Contracting Parties, each Party to the conflict shall be bound to apply, as a minimum, the following provisions: (1) Persons taking no active part in the hostilities, including members of armed forces who have laid down their arms and those placed hors de combat by sickness, wounds, detention, or any other cause, shall in all circumstances be treated humanely, without any adverse distinction founded on race, colour, religion or faith, sex, birth or wealth, or any other similar criteria. To this end, the following acts are and shall remain prohibited at any time and in any place whatsoever with respect to the above-mentioned persons: (a) violence to life and person, in particular murder of all kinds, mutilation, cruel treatment and torture; (b) taking of hostages; (c) outrages upon personal dignity, in particular humiliating and degrading treatment; (d) the passing of sentences and the carrying out of executions without previous judgment pronounced by a regularly constituted court, affording all the judicial guarantees which are recognized as indispensable by civilized peoples. (2) The wounded and sick shall be collected and cared for. 2. An impartial humanitarian body, such as the International Committee of the Red Cross, may offer its services to the Parties to the conflict. 3. The Parties to the conflict should further endeavour to bring into force, by means of special agreements, all or part of the other provisions of the present Convention. 4. The application of the preceding provisions shall not affect the legal status of the Parties to the conflict.” In the case of Nicaragua vs. The United States of America, the International Court of Justice reached a conclusion that “Article 3, as a part of customary law, constituted a minimum yard stick applicable to all armed conflicts”. (1984) The Protocol additional to the Geneva Conventions and Relating to the Protection of Victims of International Armed Conflicts expands protection to civilian population and military medical workers in international armed conflict. The Protocol additional to the Geneva Conventions and Relating to the Protection of Victims of Non-International Armed Conflict elaborates on

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victims that are caught up in internal conflicts. However, the internal conflicts must be of high intensity like civil war not riots and demonstrations. In December 2005, governments adopted the Protocol additional to the Geneva Conventions and Relating to the Adoption of an Additional Distinctive Emblem that provides for adding the Red Crystal to Emblems of International Humanitarian Law. The International Committee for the Red Cross (ICRC) ICRC has issued a statement that provides a minimum requirement in the use of machines in warfare. They said that “Any new technology of warfare must be used, and must be capable of being used, in compliance with existing rules of international humanitarian law.” It was based on the premise that article 36 of the Additional Protocol I to the Geneva Convention (1977) creates an obligation on the State parties to have a legal review prior to the use of any new weapon, during its development and acquisition for use in warfare. For the states that are not a party to the convention, the review is a “common-sense measure” to ensure safety during hostilities in warfare. ICRC believes that military applications of emerging technologies are not inevitable. It is the States that make their choices to use or not to use these technologies. However, it is clear that if they choose to use them, it must be within the bounds of existing rules, and take into account potential humanitarian consequences for civilians and for combatants no longer taking part in hostilities, as well as broader considerations of “humanity” and “public conscience”. The “principles of humanity” and the “dictates of public conscience” are mentioned in Article 1(2) of Additional Protocol I and in the preamble of Additional Protocol II of the Geneva Conventions, and referred to as the Martens Clause, which is part of customary international humanitarian law.

Conjunction with Artificial Intelligence Machine Learning Systems and Armed Conflicts Researchers and reviewers have concluded that there are three overlapping areas in which machines and AI can be used, with a humanitarian perspective. (2019) It can be: Use of the Physical Robotic System. AI has the ability to increase the use of unmanned robotic systems, in sea, air and land, armed and unarmed, automated and controlled. The manufacturing companies regard the use of such systems as beneficial, as

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machines have a better flight, navigation, surveillance, and targeting. However, ICRC believes that these autonomous weapons are an immediate concern from a humanitarian, ethical and legal perspective because giving control of attacking and selecting targets and risk of loss of human life to these machines poses a risk of unpredictable consequences. (PIPER, 2019) For this reason, it has been an initiative of ICRC to urge states that plan on using these technologies, to identify elements of human controls when using these systems. States must identify human supervision, the ability to deactivate and intervene, along with the predictability and reliability in the use of robotic systems. Similarly, the states must also look for operational constraints, the environment of use of the weapon, scope of movement, and duration of autonomous operation before using the weapons blindly. The only reason that these concerns have been shown is that these technologies, once approved, would form the basis of the use of automated weapons systems. (The Law of Armed Conflict Issues Created by Programming Automatic Target Recognition Systems Using Deep Learning Methods, 2018) Cyber and Information Warfare. Using AI in developing cyber weapons can substantially change a state’s capability to attack by looking into the opponent's vulnerabilities and exploit it or defend against unpredictable attacks. These developments can change the nature of attacks, their severity, and scale. However, the question of control of humans over this automated digital technology is stand-still. Another related application of AI is information warfare, i.e. to use of technology in spreading disinformation or misinformation. These technologies can set a higher scale in spreading this information during warfare, which may result in potentially bad consequences, amplify manipulation, and influence decision-making. Hence, the result of this disinformation can be ill-treatment or wrongful arrest of innocent civilians, denial of essential services to them, or attacks on their property. AI for decision-making in Armed Conflict. Using AI in decision-making is, perhaps, the most far-reaching application of AI in armed conflicts. AI enables widespread data collection and its analysis, it potentially assesses the pattern of behaviour and people, and thereby, recommends a proper military plans, actions, and situations. Beginning from who, when and how to attack, to whom and for how long should a prisoner of war be detained, to which weapon, including nuclear weapon, to be used during the warfare, to predicting the adversaries, the capability of using the technology in decision-making is the most questionable, from a humanitarian perspective. These AI and machine learning tools can lead to a personalized warfare. From the humanitarian perspective, the states must ensure the protection of civilians, civilian infrastructure, and services while using these digital weapons in armed conflicts, and minimize human costs. These technologies should be

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used wisely, so that unpredictability, lack of explanability and bias can be avoided. Autonomous Weapons and Lethality of AI Autonomous Weapons Systems are, generally, defined as “Any weapon system with autonomy in its critical functions - that is, a weapon system that can select (search for, detect, identify, track or select) and attack (use force against, neutralize, damage or destroy) targets without human intervention.” Using these weapons poses a potential question in international humanitarian law and international criminal law for human accountability and state responsibility. It is essential to assess the limits needed on autonomy in use of such weapons, so that compliance with IHL can be kept in check. At the Convention on Certain Conventional Weapons (CCW), in April 2016, the experts in an informal meeting highlighted that appropriate human involvement in using lethal weapons and its delegation are very important. States must ensure safety of civilians and protected persons, while using such weapons in an armed conflict. (The Chairperson of the Informal Meeting of Experts, 2016) The ICRC has called upon States to satisfy legal and ethical requirements and maintain human control over these weapons. The core components, according to ICRC, are reliability and predictability of the weapon system, human intervention in its use, knowledge about functioning and environment of use, and accountability for operation. For any autonomous weapon, the measure of human control can be inspected in three stages: (a) Development Stage, (b) Activation Stage, and (c) Operation Stage. The risks of violating IHL can be minimized by manipulating the operational parameters until its activation. In the development stage, technical design and programming of the weapon determines the extent of human control. Decisions to develop the technology must be taken in such a manner that the weapon is in compliance with IHL and international law. During the testing of such weapons, its predictability and reliability must be verified, its operational parameters must be integrated, its technical mechanism for human supervision and its ability to deactivate must be established. At the activation stage, the weapon is assessed to use for either target or respond to a general threat. If an automated weapon would operate within the constraints of IHL would depend upon the technical knowledge of that weapon. It must be ensured that the commander has sufficient knowledge and understanding of the weapon’s function for activating it. The operational knowledge of the environment, specifically potential risks to civilians and civilian objects is also essential to analyse human control during this stage. Apart from that, some operational parameters to analyse include the task which is assigned to the weapon, type of target that the weapon would attack, type of force that it would

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apply to its target, consequence of mobility of weapon in space and time frame of its operation. Similarly, if the technical and operational parameters are insufficient during the first stage to ensure compliance with IHL, the surety must be maintained at the operation stage. The only test necessary to analyse the human control is ability to intervene after activation of the weapon. (The Future of Wars: Artificial Intelligence (AI) and Lethal Autonomous Weapon Systems (LAWS), 2020) In case of violation of IHL, the legal accountability gap is still the biggest question that needs to be answered. The law of state responsibility marks the state using an autonomous weapon to be responsible for violation of IHL. However, the limits to level of autonomy and level of control over the weapons determine the internationally agreed limits and compliance with IHL. Geopolitics and AI The Fourth Industrial Revolution has brought about a major change in warfare and means of using technologies like autonomous weapons, policing robots, port systems, etc. in war. These are critical to every state’s national security, political stability and prosperity. New possibilities emerge every day, as entrepreneurs and technologists continue to innovate their current use, to bring new and different uses. Since these technologies are becoming more powerful and deeply integrated in human systems, it is essential to measure its risks and benefits, in light of the geopolitical contestation of every state. (PANDYA, 2019) Its influence caters to the public and private sector, as new fusions have applications in digital, physical as well as biological sphere. However, not every state has technological and financial capabilities to develop new means for themselves. So they resort to political measures and programs for investment in these technologies. The United States, for example, experienced the role of emergent technologies in the 1960s, for the first time. During that period, the technological gap between the USA and USSR was widening. These technologies were defusing fast in the USA and the Western Europe, due to various factors. (Directorate of Intelligence, 1969) During the Vietnam War, the then US Secretary of Defence, Robert McNamara even emphasised on use of mainframe computers, in order to analyse the military data and predict battle outcomes. (MADRIGAL, 2017) China, today, is believed to be pursuing dominance to attain military superiert and have dominance over the sphere. It is also believed that China is a threat as while using these new technologies, it is achieving strategic advantages over various spheres. They are also circumventing International Trade and Investment Rules in an unfair manner. The US believes that they are conducting espionage and threating the security and economic interests of the country and its allies. (KWANG, et al., 2020) Similarly, China too believes that

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the US is at fault. (RAHMAN, et al., 2020) On the other hand, small states like Singapore or Estonia, are leveraging these technologies to sustain their visibility in the global race of power. (JERMALAVICIUS, 2015) The problem does not end here. Digital technology has, undoubtedly, empowered some non-state organisations even more than many small states. Even though the international law seems to uphold its basic principle that nation-states constitute an international sphere, these small states are entering into a very dangerous period. The mechanism forged to resolve disputes after World War II and the Cold War seems to erode due to the emergence of these technologies. Technological bifurcation and economic decoupling are becoming likely, hence more chance of splitting the global politics and economy. Geopolitical rivalry between the big powers show little prospects of reconciliation. But the concern of IHL remains the impact of these changes on civilians. Political beliefs and nationalism has created a domestic pressure on the countries to not cooperate with each other. Bifurcation of the global politics can lead to harm the innocent civilians in many ways. AI and Transitional Justice International humanitarian law majorly aids during a subsisting armed conflict. However, amid the ongoing warfare, the civilians face numerous threats and mass human rights violations persist, globally. Since countries are at war, it is difficult for national justice systems to work usually. And after the end of the war, the violations are numerous that the regular justice system fails to respond adequately to all the victims. Therefore, countries often take initiatives to establish specialized measures that can support their transition towards peace. These measures are termed transitional justice systems and they are established with an aim to provide redress for the gross violation, contribute towards national peace and reconciliation, recognize the dignity of every individual and prevent recurrence of such incidences in the future. Common examples include truth-seeking initiatives, reparations programs, the creation of specialized criminal justice mechanisms, and institutional reform. A recent example can be drawn from the civil war of Sri Lank. In 2009, the war ended and in following years it was much discussed as to how the end of this war unfolded. However, due to mass human rights violations, the United Nations Human Rights Council (2015) suggested establishing a range of transitional justice mechanisms, like reparations commission, establishing an office on missing persons, a truth commission, and a judicial mechanism. (2019) However, to date, only one of the four promised processes, which is an office for missing persons, has been established. Evolving technology tends to intersect into every field. The prominence of using technology in warfare has led us to question how would the victims of

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this war seek justice and what would be the nature of that justice? In 2020, a two-day conference was conducted at Brookings Doha, and the main question of discussion was concerning the expansion of parameters of the “traditional transitional justice”. (HALDEMANN, 2020) One of the author highlights that “we must loosen the very conceptual strait-jacket that has for too long stifled serious thinking about the real politics of transitional justice by asking the difficult questions regarding the role of power and untidy political transactions in shaping transitions.” (HALDEMANN, 2020) Often, the transitional justice mechanisms are temporary institutes and resource intensive. New technologies have potential to offer innovative and new ways to achieve the goals in time-bound manner. Since resource document gathering, content analysis, and selection and extraction of relevant information are crucial steps of the mechanism, if this task is undertaken without the aid of technology, it would take many people and enormous amount of time. In Sri Lanka, for example, researchers involved many students and dozens of pro bono lawyers to manually search and read over six thousand documents online for Conflict Mapping and Achieve Project. (2017) Therefore, automated systems are effective solution for this problem. AI and machine learning is operative in this task. Though these technologies have been existing for decades, it is unclear how these technologies are being used in this area. There is no denial that manual databases are valuable, though they are not even remotely near to what is technologically possible in this day. Twenty years ago, a renowned author, Ruti Teitel, described transitional justice as “a project of bounded justice – limited and partial, normative without illusions, yet nurturing some small hope of amelioration.” (GAVSHON, 2019) Her realistic yet hopeful vision remains as relevant now as it was then. More often, agencies use interactive data visualization techniques and it facilitates ease of understanding because people understand complex information better through visualization. Therefore, the need to understand the depth of use of technology, not just to ease the working but also to understand complex situations, is yet to be discovered.

Case Studies Project Maven by Pentagon Project Maven is an initiative of the United States Department of Defense which attempts to turn enormous volumes of data processing into actionable intelligence and insights. In 2017, the department announced to form an Algorithmic Warfare Cross Functional Team (AWCFT), which was tasked to manage all the data collected from drone surveillance. The aim was to accelerate

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integration of big data and machine learning into military affairs, so that the massive data that was collected during the fight against ISIS can be turned into actionable intelligence to enhance decision-making. Researchers wanted to expand their work to numerous areas like analysis of vertical image and video to document analysis, horizontal still photo video object and persona identification, natural language processing for machine translation and gisting; optical character recognition, cognitive computing for target systems analysis and entity relationship identification and other areas using machine learning approaches. Hence, numerous business that are involved in artificial intelligence and machine learning were invited into the project. Tech giants like Google also took part and provided its aid and assistance in the project. However, soon people realized that there is a possibility that the United States government is trying to create an autonomous weapons system that could fire without a human operator. So, the Google employees, engineers and researchers protested against the involvement of company in the project because of a much-debated possibility of harm in using AI for military purposes. Soon, the company and the Department of Defense issued a statement clarifying that “any military use of machine learning naturally raises valid concerns. Therefore, we’re actively engaged across the company in a comprehensive discussion of this important topic. Such exchanges are hugely important and beneficial and specifically scoped to be for non-offensive purposes”. In June 2018, Google announced that it is pulling out of the Pentagon Program. (GRIFFITH, 2018) Google’s withdrawal from the project market a huge roadblock in this project. It was also alleged by various people that this withdrawal was, in fact, due to Google’s involvement with Chinese government. However, in December 2019, Palantir, the surveillance company founded by Peter Thiel took over the project and its status is still unknown. (GREENE, 2019) Drones and Robots in the Military • In May 2020, the Australian government announced that they have locally manufactured their first artificial powered 38-foot-long drone, “Loyal Wingman”, with a range of almost 2000 miles. It was reported that the drone would be engaging in electronic warfare as well as intelligence, reconnaissance and surveillance missions. • China appears to be the most ahead with its massive domestic surveillance programs and military drones that have capability to ferry passengers. • The Russians are also engaging in increasing their military capability and by 2030, they are expected to encompass AI throughout their combat capacity,

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including AI-guided missiles that would have capability to change their target mid-flight. • Israel, too has incorporated and adopted a vast targeting network and AIinfused missiles for patrolling in remote regions. • South Korea has launched its own AI lab and used sentry robots for militarizing the zones at the border of North Korea.

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International Criminal Law

Mridutpal Bhattacharya and Arundhati Kale

Introduction & Legal Background International Criminal Law deals in criminal liabilities & of individuals or organizations with respect to the gravest of offences, against human rights & violations of international humanitarian laws. Primarily, offences deserving of being considered under the purview of International Criminal Law are war crimes, crimes against humanity, genocide & crimes of aggression. International Criminal Law (ICL) can be referred to as a relatively new & everevolving branch of Public International Law (PIL). The ICL causes the criminalization of the gravest of violations of human rights & International Humanitarian Laws, in order to assign criminal liability to perpetrators of the said offences. The principles of ICL apply to all perpetrators, including but not limited to those involved in the planning & authorization of such offences as well as those who are engaged in firsthand commission of the crimes. Therefore, the principles call for a relevant option of holding accountable the individuals holding the highest of profiles in even the political or military ranks which are outside of the purview of criminal law of the civilians – the criminal law that we all are so well acquainted with. Criminal accountability for these gravest of offences is of rudimentary essentiality with respect to the Rule of Law, potential deterrence of future violations, & the provisions of redress & Justice for the victims of the said offences & offender(s). However, it is to be borne in mind that – all violations of International Law &/or International Humanitarian Law are not is essence considered to be of a criminal nature. Only the most serious of violations of International Human Rights Law (IHRL) & International Humanitarian Law (IHL) are regarded as International Crimes worthy of being granted the unto the purview of International Criminal Law. Examples would include International Crimes in the spectrum of war crimes, crimes of aggression, crimes against humanity, & genocide. International crimes are justly considered to be affective of the ideologies, sentiments & natures of international communities as wholes. Consequently, supplementary to the prevention of occurrences of these grave & heinous crimes, all States possess an equal interest in restraining/subduing/eliminating the perpetrator while holding him or her or them or it accountable. Crimes in

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the likes of civilian massacres, rapes, forcible transfers, torture, indiscriminate bombings, apartheid & persecution are all equally, if not more violate the most fundamental principles of humanity, morality & dignity. In the last century – the international community has reaffirmed the significance of the protection of these inherently elementary values by means of prohibition of the aforesaid conducts & ensuring the liabilities & accountabilities of the entities committing those acts. The primary classifications of such international crimes are: • Genocide - Article 6 of the Rome Statute of the International Criminal Court considers “genocide” to mean any of these acts committed with intentions to destroy, wholly or in partly, national, ethnical, racial or religious groups (International Criminal Court, 2002): ─ Killing members of the group (International Criminal Court, 2002); ─ Causation of serious bodily or mental injury to members of the group(s) (International Criminal Court, 2002); ─ Deliberate infliction of conditions of life premeditated to bring about physical destruction in whole or in part to the group(s) (International Criminal Court, 2002); ─ Imposition of measures intended towards the prevention of births within the groups(s) (International Criminal Court, 2002); ─ Forcible transference of the children of the group to another group (International Criminal Court, 2002). The term “Genocide” is often mistakenly used in common practice as the crime is not necessarily defined by numbers or outcome, but is defined instead by the specific intent of the perpetrators intending to not only harm the victims but to cause destruction of the groups as such. The crime has been codified in the UN Genocide Convention; the Rome Statute of the International Criminal Court (ICC) & is further elaborated upon in the Jurisprudence of the International Criminal Tribunal for Rwanda. • Crime against Humanity - Article 7 Paragraph 1 of the Rome Statute of the International Criminal Court considers “Crimes against humanity” to mean any of these acts which if & when committed as part of a widespread or systematic attack aimed at civilian populations, with knowledge of the attack attracts criminal accountability (International Criminal Court, 2002) – ─ Murder (International Criminal Court, 2002); ─ Extermination (International Criminal Court, 2002); ─ Enslavement (International Criminal Court, 2002); ─ Deportation or forcible transfer of population (International Criminal Court, 2002);

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─ Imprisonment or any other forms of severe deprivation of physical liberty infringing fundamental rules of international law (International Criminal Court, 2002); ─ Torture (International Criminal Court, 2002); ─ Sexual offenses such as rape, enforced prostitution, sexual slavery, forced pregnancy, enforced sterilization or any other offense comparable in gravity to the aforementioned (International Criminal Court, 2002); ─ Oppression of any distinguishable group or congregation on racial/political/national/ ethnic/religious/cultural/orientation grounds, or other grounds generally accepted as impermissible per international law, with regards to any act referred to or any offense within the jurisdiction of the Court (International Criminal Court, 2002); ─ Enforced disappearance of persons (International Criminal Court, 2002); ─ The crime of apartheid (International Criminal Court, 2002); ─ Any other inhumane acts similar in character to the ones previously mentioned, with intent to intentionally cause great suffering, or grave injury to the body or mind. (International Criminal Court, 2002) Crimes against humanity condense several acts which amount to serious violations of human rights when committed as part of a widespread or systematic attack against a civilian population. Numerous Courts, along with the International Criminal Court, require the act to be the part of a governmental or organizational policy. The crimes against humanity include but are not limited to acts such as torture, murder, extermination, enslavement, deportation or forcible transfer, sexual violence, persecution & the crime of apartheid. The definitions of all the types of attacks are provided in Article 7 Paragraph 2 of the Rome Statute. • War Crimes - Article 8 Paragraph 2 of the Rome Statute of the International Criminal Court considers “war crimes” to mean: ─ Grave breaches of the Geneva Conventions, i.e. any of the following acts against individuals or any property sheltered via the provisions of the relevant Geneva Convention(s) (International Criminal Court, 2002): ─ Willful Killing (International Criminal Court, 2002); ─ Torture or inhuman treatment, inclusive of biological experiments (International Criminal Court, 2002); ─ Willful causation of grave suffering, or serious bodily or mental injury (International Criminal Court, 2002); ─ Wide-ranging destruction and misappropriation of property which is not justified by military necessity and is carried out in an unlawful and wanton manner (International Criminal Court, 2002);

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─ Compelling prisoner(s) of war or any other protected individuals towards servitude in the forces of a hostile Power (International Criminal Court, 2002); ─ Willful deprivation of prisoner(s) of war or other protected individuals of the rights of fair and regular trial (International Criminal Court, 2002); ─ Unlawful deportation or transfer or unlawful confinement (International Criminal Court, 2002); ─ Taking of hostages (International Criminal Court, 2002). ─ Supplementary grave violations of laws and customs pertinent in international armed conflict(s), under the purview of recognized framework(s) of international law, i.e. any of the following acts (International Criminal Court, 2002): o Intentional directing of attacks towards the civilian populace or against individual civilians who is not a direct party of/in hostility (International Criminal Court, 2002); o Intentional directing of attacks towards civilian objects, i.e., objects which are not military objectives (International Criminal Court, 2002); o Intentional directing of attacks towards personnel, installations, material, units or vehicles for humanitarian assistance or peacekeeping missions in concurrence to the Charter of the United Nations, on the assumption that of their entitlement to the protection awarded to civilians or civilian objects under the international law of armed conflict (International Criminal Court, 2002); o Premeditated initiation of an attack knowing that such attack will give rise to accompanying loss of life/injury to civilians/damage to civilian objects/ widespread long-term and severe damages to the natural environments which would undoubtedly be unwarranted with reference to the concrete and direct overall advantage projected by the military (International Criminal Court, 2002); o Attacking or bombarding of towns/villages/dwellings/buildings which are undefended & are not military objectives via whatever means (International Criminal Court, 2002); o Killing or wounding of a combatant who - having laid down his arms or having no longer any means of defense has surrendered at discretion (International Criminal Court, 2002); o Making indecorous usage of the flag of truce/of the flag/the military insignia and uniform of the enemy/the United Nations/the distinctive emblems of the Geneva Conventions - resulting in death or serious personal injury (International Criminal Court, 2002); o The transference - directly or indirectly, by the Occupying Power of fractions of its civilian populace into the territory occupied by it/ the

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o o o o o

o o o o

deportation or transfer of all or fractions of the populace of the occupied territory within or outside the territory (International Criminal Court, 2002); Premeditated directing of attacks towards buildings dedicated towards arts/ education/religion/science/charitable purposes/historic monuments/hospitals and places where the sick and wounded are congregated – while such buildings are not military objectives (International Criminal Court, 2002); Subjecting individuals under the power of a hostile party to physical mutilation(s) or to medical or scientific experiment(s) of any sort that are neither justified by the medical, dental or hospital care of the person, nor done with his or her interest in mind, and which cause death to/seriously compromise the wellbeing of such person/persons (International Criminal Court, 2002); Killing or wounding individuals belonging to the hostile nation or army deceitfully (International Criminal Court, 2002); Announcing that no quarter will be given (International Criminal Court, 2002); Destroying or confiscating the enemy’s property unless such destruction or confiscation is an imperative demand of the necessities of war (International Criminal Court, 2002); Announcing the suspension/abolishment/inadmissibility with regards to a court of law the - rights and/or action(s) of the members of the hostile party (International Criminal Court, 2002); Coercing of a member of the hostile party towards having an active part in the operations of war directed against their country, even when they have been in the other side’s service prior to the commencement of the war (International Criminal Court, 2002); Pillaging of a town/place, even when taken through assault (International Criminal Court, 2002); Employing poison or poisoned weapons (International Criminal Court, 2002); Employing asphyxiating/poisonous or other gases, and all analogous liquids/ materials/devices (International Criminal Court, 2002); Employing bullets which expand or flatten effortlessly in the human body, such as bullets with hard envelopes which do not entirely cover the core or are pierced with incisions (International Criminal Court, 2002); Employing of weapons/ projectiles and/or material(s) and method(s) of warfare which are predominantly of a nature causing unessential injuries or needless anguish or which are inherently indiscriminate in desecration of the international laws of armed conflict, when such

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weapons/projectiles/and/or material and methods of warfare are subject(s) of comprehensive prohibitions and are enclosed in an annex to the Statute, via any amendment in agreement to the relevant provisions set out in articles 121 and 123 (International Criminal Court, 2002); Committing barbarities upon personal dignity - in particular, humiliating and degrading treatment (International Criminal Court, 2002); Committing and/or causing enforced prostitution/rape/sexual slavery/forced pregnancy as established in article 7 - paragraph 2(f)/enforced sterilization/ or any other form(s) of sexual ferocity constituting a momentous breach of any provision of the Geneva Convention(s) (International Criminal Court, 2002); Exploiting the presence of a civilian or other protected person to render points/ areas/military forces immune to military operations (International Criminal Court, 2002); Deliberately using famishment of civilians as a scheme of warfare by divesting them of objects crucial to their survival, including deliberately hindering relief supplies as provided for under the Geneva Convention(s) (International Criminal Court, 2002); Recruiting or conscripting children aged less than fifteen years, into the national armed forces or making/allowing them to participate actively in hostilities (International Criminal Court, 2002).

In case of armed conflict(s) not having an international character, somber violations of article 3 shared by the four Geneva Conventions of 12 August 1949, i.e., any of the following acts committed against individuals having no active part in the hostilities, including members of armed forces who have surrendered and places hors de combat by sickness/wounds/detention or any other cause (International Criminal Court, 2002): ─ Violence towards life and/or person - in particular murder of all sorts/ mutilation(s)/cruel treatment/torture (International Criminal Court, 2002); ─ Committing barbarities against personal dignity - in particular humiliating/degrading treatment (International Criminal Court, 2002); ─ Taking of hostages (International Criminal Court, 2002); ─ The pronouncements of sentences/carrying out of executions without prior judgment pronounced by a habitually established court, affording all judicial guarantees which are commonly considered to be indispensable (International Criminal Court, 2002).

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Paragraph 2(c) relates to armed conflicts that are not of an international character and thus does not apply to situations pertaining to internal disturbances and/or tensions, such as riots/isolated and sporadic acts of violence/ other acts of a similar nature (International Criminal Court, 2002). Other grave violations of the laws and customs pertinent in armed conflicts not of an international character, within the established framework of international law, i.e. any of the following acts (International Criminal Court, 2002): ─ Intentionally directing attacks towards the civilian populace as such or towards individual civilians who are not direct parties in hostilities (International Criminal Court, 2002); ─ Intentionally directing attacks towards building/material/medical units/transport/personnel using the distinctive emblems of the Geneva Conventions in concurrence with international law (International Criminal Court, 2002); ─ Calculatedly leading attacks on installations/personnel/units/materials/vehicles involved in peacekeeping missions or humanitarian efforts in concurrence to the United Nations Charter, when they are eligible for being protected as civilians/civilian objects under the purview of international law(s) of armed conflict(s) (International Criminal Court, 2002); ─ Calculatedly leading attacks at buildings dedicated to arts/ science/religion/education/charitable purposes/historic monuments/hospitals and places where the sick and wounded are congregated, as long as they are not a part of military objectives (International Criminal Court, 2002); ─ Pillaging a town/place, even when overrun by assault (International Criminal Court, 2002); ─ Committing rape/sexual slavery/enforced prostitution/forced pregnancy as defined in article 7 - paragraph 2 (f)/ enforced sterilization/any other form of sexual violence also constituting a serious violation of article 3 common to the four Geneva Conventions (International Criminal Court, 2002); ─ Recruiting or conscripting children under fifteen years into armed forces or groups or making/allowing them to participate actively in hostilities (International Criminal Court, 2002); ─ Causing/ordering shift of civilian populace for reasons related to the conflict, unless the sanctuary of such civilians involved is compromised/or imperative military reasons are so demanding. (International Criminal Court, 2002);

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─ Killing or wounding a combatant adversary deceitfully (International Criminal Court, 2002); ─ Announcing that no quarter will be given (International Criminal Court, 2002); ─ Exposing persons who are under the power of another party to the conflict - to physical mutilation(s)/medical or scientific experiments of any kind which are neither justified by the dental/mental/hospital care of the concerned person, nor done in his or her interest, and which cause death or seriously jeopardizes the wellbeing of such person or persons (International Criminal Court, 2002); ─ Destroying or confiscating the property of an adversary lest such destruction or confiscation is demanded imperatively by the necessities of the conflict (International Criminal Court, 2002); ─ Employing poison/poisoned weapons (International Criminal Court, 2002); ─ Employing asphyxiating/poisonous/or other gases, and all analogous liquids/ materials/devices (International Criminal Court, 2002); ─ Employing bullets which expand/flatten effortlessly in the human body, like bullets having hard envelopes which do not wholly conceal the core or are perforated with incisions (International Criminal Court, 2002). Paragraph 2 (e) relates to armed conflicts that are not of an international character and thus does not apply to situations of internal disturbances/tensions, such as riots/ isolated and sporadic acts of violence/other acts of a similar nature. It relates to armed conflict(s) occurring in the territory of a State while there’s also long-drawn-out armed conflict(s) between administrative powers that be and/or organized armed assemblies or between such assemblies. (International Criminal Court, 2002) Nothing in paragraph 2 (c) and (e) affects the responsibility of a Government towards maintenance or reestablish law and order in the State or to defend the unity and territorial integrity of the State, via all legitimate means. (International Criminal Court, 2002) Article 147 of the Convention (IV) regarding Protection of Civilian Persons in Time of War – under Penal Sanction II - Grave Breaches states that – “Grave breaches…shall be those involving any of the following acts, if committed against persons or property projected by the present Convention: willful killing, torture, or inhuman treatment, including biological experiments, willfully causing great suffering or serious injury to body or health, unlawful deportation or transfer or unlawful confinement of a protected person, compelling a protected person to serve in the forces of a hostile Power, or willfully depriving a protected person of the rights of fair and regular trial prescribed in the present Convention, taking of hostages and extensive destruction and appropriation of property, not justified by military necessity and carried out unlawfully and wantonly.” (International Committee of the Red

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Cross)

Article 50 of the Convention (I) regarding Amelioration of the Condition of the Wounded and Sick in Armed Forces in the Field – under Grave Breaches states that – “Grave breaches…shall be those involving any of the following acts, if committed against persons or property protected by the Convention: willful killing, torture or inhuman treatment, including biological experiments, willfully causing great suffering or serious injury to body or health, and extensive destruction and appropriation of property, not justified by military necessity and carried out unlawfully and wantonly.” (INTERNATIONAL COMMITTEE OF THE RED CROSS)

Article 51 of the Convention (II) regarding Amelioration of the Condition of Wounded, Sick and Shipwrecked Members of Armed Forces at Sea – under Grave Breaches states that – “Grave breaches…shall be those involving any of the following acts, if committed against persons or property protected by the Convention: willful killing, torture or inhuman treatment, including biological experiments, willfully causing great suffering or serious injury to body or health, and extensive destruction and appropriation of property, not justified by military necessity and carried out unlawfully and wantonly.”

Article 130 of the Convention (III) regarding Treatment of Prisoners of War – under Grave Breaches states that – “Grave Breaches…shall be those involving any of the following acts, if committed against persons or property protected by the Convention: wilful killing, torture or inhuman treatment, including biological experiments, wilfully causing great suffering or serious injury to body or health, compelling a prisoner of war to serve in the forces of the hostile Power, or wilfully depriving a prisoner of war of the rights of fair and regular trial prescribed in this Convention.” (International Committee of the Red Cross)17

Article 85 of the Protocol Additional to the Geneva Conventions of 12 August 1949, regarding Protection of Victims of International Armed Conflicts (Protocol), 8 June 1977 – under Repression of Breaches of this Protocol states that – 1. The provisions of the Conventions relating to the repression of breaches and grave breaches, supplemented by the Section, shall apply to the repression of breaches and grave breaches of this Protocol (International Committee of the Red Cross). 2. Acts described as grave breaches in the Conventions are grave breaches of this Protocol if committed against persons in the power of an adverse Party protected by Articles 44, 45 and 73 of this Protocol, or against the wounded, sick and shipwrecked of the adverse Party who are protected by this Protocol, or against those medical or religious personnel, medical units or medical transports which are under the control of the adverse Party and are protected by this Protocol (International Committee of the Red Cross). 17

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Individual criminal responsibility and modes of liability Culprits in cases of genocides, crimes against humanity and war crimes can be held individually criminally liable along with the State. For e.g., the former Republic of Yugoslavia & a few individual political leaders and military commanders were accused of genocide during the wars in the former 3. In addition to the grave breaches defined in Article 11, the following acts shall be regarded as grave breaches of this Protocol, when committed wilfully, in violation of the relevant provisions of this Protocol, and causing death or serious injury to body or health (International Committee of the Red Cross): (a) making the civilian population or individual civilians the object of attack; (b) launching an indiscriminate attack affecting the civilian population or civilian objects in the knowledge that such attack will cause excessive loss of life, injury to civilians or damage to civilian objects, as defined in Article 57, paragraph 2 (a)(iii); (c) launching an attack against works or installations containing dangerous forces in the knowledge that such attack will cause excessive loss of life, injury to civilians or damage to civilian objects, as defined in Article 57, paragraph 2(a)(iii); (d) making non-defended localities and demilitarized zones the object of attack; (e) (e)making a person the object of attack in the knowledge that he is ‘hors de combat’; (f) the perfidious use, in violation of Article 37, of the distinctive emblem of the red cross, red crescent or red lion and sun or of other protective signs recognized by the Conventions or this Protocol. 4. In addition to the grave breaches defined in the preceding paragraphs and in the Conventions, the following shall be regarded as grave breaches of this Protocol, when committed willfully and in violation of the Conventions or the Protoco (International Committee of the Red Cross)l: (a) the transfer by the Occupying Power of parts of its own civilian population into the territory it occupies, or the deportation or transfer of all or parts of the population of the occupied territory within or outside this territory, in violation of Article 49 of the Fourth Convention; (b) unjustifiable delay in the repatriation of prisoners of war or civilians; (c) practices of ' apartheid ' and other inhuman and degrading practices involving outrages upon personal dignity, based on racial discrimination; (d) making the clearly-recognized historic monuments, works of art or places of worship which constitute the cultural or spiritual heritage of peoples and to which special protection has been given by special arrangement, for example, within the framework of a competent international organization, the object of attack, causing as a result extensive destruction thereof, where there is no evidence of the violation by the adverse Party of Article 53, sub-paragraph (b), and when such historic monuments, works of art and places of worship are not located in the immediate proximity of military objectives; (e) depriving a person protected by the Conventions or referred to in paragraph 2 of this Article of the rights of fair and regular trial. 5. Without prejudice to the application of the Conventions and of this Protocol, grave breaches of these instruments shall be regarded as war crimes (International Committee of the Red Cross).

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Yugoslavia. Perpetrators committing international offences may be convicted on the basis of their acts or omissions, or their ordering or facilitation of the said offense. In case where international crime comprise an aspect of a broad & complex state policy, wide ranges of offenders might be convicted under different modes of liability by international criminal courts & tribunals, the most common of which are: • Committing (act or omission) – Physical infliction of a crime via an act or culpable omission; • Indirect perpetration – Utilization of another person for the physical causation of a crime, while being in control of the will of the direct perpetrator; • Joint Criminal Enterprise (JCE) – The contribution of or to criminal activity by several individuals with a common purpose, that is fulfilled either jointly or by some members of this plurality of perpetrators; • Co-perpetration – Involvement through direct modes in the commission of the said offense without being a principal actor necessarily. E.g. membership in a gang that attacks an individual, without in fact being directly attached to the attack; • Aiding & Abetting – Substantial contribution to the carrying out of the said offense; • Planning – Contemplation, planning & designing of the crime, regardless of whether the crime is in fact committed; • Ordering – Utilization of a de juro or de facto authority for purposes of instructing another offender towards the commission of an offense; • Superior/Command Responsibility – Failure on the part of a superior in relation to the prevention or punishment of the commission of a crime by a subordinate. • Conspiracy – Agreement to the commission of an offense, in international law refers to the crime of conspiracy only in relation to genocide; • Incitement – Instigation, induction, encouragement, or persuasion of the perpetrator towards the crime; Universal Jurisdiction In reference to International law, & the principle of Universal Jurisdiction, all States are free to prosecute the perpetrator of genocide, crimes against humanity or war crimes in their very own national courts;

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Conjunction with AI Machine learning and artificial intelligence is commonly seen as a positively disruptive branch of data science whose extension allows for improvements in the speed, efficiency, and reliability of decision-making, and whose potential impacts myriad arenas of human activity. A particular focus for development id cast upon the criminal justice sector, particularly international criminal justice, where AI is an avenue to filter evidence from digital media, to perform visual analyses of satellite data, or to conduct textual analyses of judicial reporting datasets. This has implications for adjudication and rational legal fact-finding, especially after the implementation of forensic and investigatory forms of AI within the criminal justice, and its subsequent introduction to the international criminal courtroom by way of expert opinion evidence. Since AI is being used as a tool in heterogenous arenas of work, the international criminal justice arena sees, perhaps, a significantly fuller utilisation of AI driven efficiencies. The receptivity by the international criminal justice (ICJ) sector can be perceived via responsiveness of the courts, when faced with evidence drawn from ‘open source’ data. “Federal law enforcement agencies…are primarily using three types of forensic algorithms to help assess whether or not evidence collected in a criminal investigation may have originated from an individual: probabilistic genotyping, latent print analysis, and face recognition.”

If a data so generated by AI is admissible in court, it may easily be categorized as a class of documentary evidence (or even as a form of witness testimony, since AI is being widely seen as artificial human beings). Artificial intelligence has the potential to be a permanent part of our criminal justice ecosystem, providing investigative assistance and allowing criminal justice professionals to better maintain public safety. We see how scientists are in the process of developing a sophisticated computer algorithm to track down paedophiles just by using the image of their hands. The five-year research project 'H-Unique' is based on the scientific fact that human hands are unique, and thus, can be used to identify any individual, much like tongue prints or fingerprints. These photos will be used to train the algorithm to catch child abusers from videos posted online. Researchers make use of vein patterns, skin creases, scars, freckles, moles and knuckle creases to identify individual hands. Studies have proved they are different between our right and left hands, and even different between identical twins. Taking that uniqueness into the courtroom would make juries more certain about whether to convict someone of a crime or exonerate someone who is innocent. (The Week, 2020)

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Yet another case which arose before the Special Tribunal for Lebanon ("STL"), Prosecutor v. Ayyash had many firsts for international criminal justice, both procedurally and legally. While the prosecution's case was purely circumstantial with no direct evidence, the case still involved the first use of telecommunications data as evidence. An explosion in front of the St. Georges Hotel, Beirut on 14 February 2005, which destroyed and killed a convoy of vehicles carrying the former Prime Minister of Lebanon, Rafik Hariri along with eight members of his entourage and 13 bystanders, as well as injuring over 200 others. The investigations saw two different parties attempting to get to the crux of the blast: the local police and the United Nations, each carrying out their own investigations. One of the local police officers, Wissam Eid, with a background in computer engineering, took a different approach: he decided to look at cell phone records. (Digital Evidence and War Crimes Prosecutions: The Impact of Digital Technologies on International Criminal Investigations and Trials, 2017-18) The case sees a very early version of machine usage to calculate and pinpoint the area and number of explosives used, along with the place of detonation. The prosecution presented expert reports and testimony from two Argentinian civil engineering professors who used specialized computer programming and algorithms to input and determine what it would take to cause the damage to the public square. This was the first time that explosion reconstruction had been used in an international criminal tribunal. They explained how they ascertained the quantity of the explosives as well as their location in terms of height above ground of the explosive mass, concluding that, given the parameters of the crater, the damage observed, measured and numerically modelled could have only been created by an above-ground explosion. They ran simple simulations taking into account multiple scenarios to explain the size of the crater and the structural damage thus caused. The reports and testimony supported the theory that the explosion was the work of a suicide bomber driving a van carrying explosives, as opposed to the defense’s claim that the blast was caused by an underground bomb. Since the amount of data points entered into the calculation and the ability to produce different scenarios was beyond human capacity, it necessarily required machine intelligence intervention. Thus, we see that while the defense presented a counter-expert, it will be very difficult to challenge the findings of the computer program without the ability to truly understand how the algorithm works. This is one of the biggest challenges while admitting AI based evidence. Concerns have arisen regarding the potential for algorithms and machine learning systems to exhibit ‘algorithmic bias’, since they are ultimately by-products of human innovation and thus susceptible to similar prejudices a human might have, entrenched in socio-economic and racial inequalities. These inequalities are very much visible in the functioning of an algorithmic

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system, providing basis for its output decision. Similar concerns have arisen around the tendency of algorithmic systems to display behaviours which display significant deficiencies with regard to discernibility, predictability, and tractability. These materialise around the concept of ‘algorithmic opacity.’ As defined by Burrell, algorithms are opaque to the extent that ‘…if one is a recipient of the output of the algorithm (the classification decision), rarely does one have any concrete sense of how or why a particular classification has been arrived at from inputs.’ (Richmond, 2020) Opacity is thus often translated to the concept of ‘algorithmic transparency,’, with demands introduction of public, non-proprietary ‘open source’ systems. These epistemic issues are deeply rooted within the field of forensic science, and criminal justice, where the ‘black-boxing’ of algorithmic classifications may require the trier-of-fact to accept expert assertions, absent of meaningful examination and evaluation, while simultaneously concealing problems relating to the foundational validity of novel scientific methods. The defense cannot cross-examine a computer program. It further requires specialized knowledge to interpret evidence and analyse it. This poses as an obstacle in the defense’s ability to counteract the prosecution’s case. AI & Evidence Law

Evidence law attempts to narrow the evidence, which may be presented at a trial, in two possible ways: • To bound evidence to what is relevant; • To prevent the court from arriving at illogical conclusions and minimize unfairness towards the accused; In today’s technology-oriented era, machines and gadgets are being used to supplement testimonies. Erratically, courts have considered such evidence to have exceeded beyond the intentions of evidentiary rules (and have thus, refrained from admitting them in court). Despite appearing neutral in their opinion, they are essentially a product of human creation, and therefore, absorb human notions of judgement, which may be biased. A distinction to be drawn is imperative, when one considers general technology evidence from AI generated evidence. Digitized Breathalyzers record the levels of alcohol. But a more sophisticated machine- say an automated car which detects the sleep levels of a driver via analysis of their body language, dip in body temperatures, patterns of driving- have an underlying issue: How the data and the evidence generated by an AI mechanism afford reliability, since the conclusions they draw, although simple on the face of it, actually remains largely shrouded in mystery. As such, jurors cannot peer

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into the “black box” which holds the evidence. The fact-finding procedure, and assessment of evidentiary reliability, is a human-focused singularity which aims to provide transparency and objective information to the trier of fact and at the same time, safeguard a reliable and valid fact-finding process. Therefore, the use of channelled AI in forensic instruments poses challenges to evidentiary law and the appraisal of fact. For instance, digitized breathalyzers have shed light on issues surrounding these types of evidentiary assistants that contain inherent black-box problems—an inability to adequately explain their inner workings (Regulating Artificial Intelligence Systems: Risks, Challenges, Competencies and Strategies, 2016). Thus, a machine may erroneously inculpate. But on the whole, Law has been amenable to newer technology for their evidentiary uses. If anything, machine generated proof has enhanced objectivity and relative accuracy in fact finding. Many emerging applications of machine learning in the law of evidence are prevalent. Risk assessment in parole hearings is being achieved via regression analysis (regression analysis refers to a set of statistical processes for gauging the relationships between a dependent variable /outcome variable and one or more independent variable.). Facial recognitions identify an accused even in less ideal circumstances. Body recognition algorithms are useful where no photographic images are available. Thus, as technology evolves, the gambit of application of AI-generated evidence expands exponentially. If a data so generated by AI is admissible in court, it may easily be categorized as a class of documentary evidence (or even as a form of witness testimony, since AI is being widely seen as artificial human beings). Digital analytical tools (DNA testing for example) in forensic settings are set apart from first generation (like dactyloscopy or graphology), since they have an underlying source code which operates them, rather than just human expertise. But because of this, their operations are more difficult to see at work. This lack of transparency creates risks of trustworthiness, especially in criminal proceedings, where flaws are less discernible by fact finders. But the underlying technology behind DNA testing and that of AI generated evidence is different, since AI is far more superior in its functioning, allowing tasks like monitoring, traceability, evaluation of human behaviour and thus are able to act independently. Hence, they are usually classified as third generation forensic evidence. Admissibility under the US Federal Law: An Exemplication Under the US federal law, machine learning output is used as a substantive evidence, and as such forms a part of expert testimony, under Rule 702 and the Daubert Criteria. How far is it admissible in a state depends on an individual

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state’s rules of evidence. A qualified expert testimony necessarily has the following ingredients: • Testimony so flourished must aid the trier of fact; • The opinion is backed and based on sufficient facts or data; • Such testimony is reliable, brought out by dependable methods and principles; • The findings are applicable to the instant case at hand. While determining the admissibility of such testimony, a judge makes a “preliminary assessment of whether the reasoning or methodology underlying the testimony is scientifically valid and of whether that reasoning or methodology properly can be applied to the facts in issue.” Thus, focus is not impressed on the conclusions so derived from the methodology of the testimony. However, the exact manner in which the algorithm was created or the way it would be used at trial may, in some cases, render it inadmissible. Daubert Criteria (1993). The Daubert Criteria is a set of rules which determine the admissibility of expert testimony. In Daubert v. Merrell Dow Pharmaceuticals, the US Supreme Court laid a generalized framework for courts to evaluate if expert testimony is the result of “reliable principles and methods” under Rule 702. It lists four considerations which do not explicitly prohibit machine learning evidence: • The task of ensuring that scientific testimony truly arises out of scientific procedure rests on the trial judge i.e. the judge is the “gatekeeper”. • A judge must ensure that that the testimony so flourished is more likely than not reliable and based on reliable methods. Such testimony can be reliably applied to the instant case. • A theory or technique must be generally accepted in the field or scientific community. i.e., scientific methodology. • Illustrative factors: while defining scientific methodology, if furnished a set of illustrative factors (not tests) in determining if these criteria are met; ─ Whether the theory or technique employed by the expert is generally accepted in the scientific community (point C above) ─ Whether it has been subjected to peer review and publication; ─ Whether it can be and has been tested (part of point A); ─ Whether the known or potential rate of error is acceptable; and ─ Whether the research was conducted independent of the particular litigation or dependent on an intention to provide the proposed testimony The above five subpoints thus create the crux of the Daubert Standard. Machine learning, off the bat, satisfies three of the factors. Machine learning

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can very easily be tested, since the resulting output can be shown as false. For e.g., Back in 2015 (Zhang, 2015), a Brooklyn based man was shocked to find a google album labelled as “gorillas”, holding series of pictures with him and his friends, effectively calling both of them primates. Yonathan Zunger, Google’s chief architect of social, tweeted, “Until recently, [Google Photos] was confusing white faces with dogs and seals. Machine learning is hard.”. The error was quickly rectified. Brian Bracken, CEO of the facial recognition company Kairos stated that if not taught properly, machine learning could make culturally inappropriate assumptions, making it quite similar to a child. Similarly, Nikon’s face detection technology was accused of being racist, when an Asian face was being clicked, with the message appearing “Did someone blink?” even though no one did. Funnily, despite being a Japanese company, Nikon failed to create a camera keeping Asian features in mind. Daubert’s peer review consideration is also fulfilled by machine learning as it has grown rapidly in recent years with some of its principles and methods, reaching back to the mid-20th century. Moreover, machine learning is vastly accepted in both field and scientific practitioners, where they apply this technology in varied forms. Daubert’s necessity that science have either know or have possible known error rates gives a more intricate analysis. Machine learning algorithms have discernible error rates, even if the relevance of these error rates to specific situations can be open to doubt. Rule 702(d)’s requirement that the evidence be “reliably applied the principles and methods to the facts of the case.”102 If an algorithm has an impressive rate of error with respect to data that bears little resemblance to the instant defendant, then its conclusions are not being reliably applied to the facts of the case. Afterall, AI has been known to falsely recognise dark skinned people (as seen above) and AI speech recognition mechanisms face difficulties in recognising English tainted with accents other than American or British. An English-speaking Irishman faces common problems of understanding when communicating with his AI tool. Thus, in such cases, it implies that the data set from which AI learns is prejudiced. The level of technology determines how the machine is classified; as silent witness (like analogue videos), digital analytical tools (DNA Testing) and AI driven mechanisms, which flourish independent evaluations of situations. Experts use such evidence in criminal trial, especially defence attorneys (although, as time progresses, one will eventually the ai generated evidence being used in prosecution cases, as happens with any new development in technology. We see how DNA testing eventually shifted from mere defence purposes to being used for prosecution. They must use and depict how data is registered and make it explainable, such that how that data impacts machine learning and avenues of error. It may be used as expert evidence in adversarial proceedings, used as concrete and supplementary evidence (partisan presentation of a case). In terms of investigative proceedings, prosecutor may

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employ experts to add to the, or, affirm the findings mentioned in a case file. Such reports and case file make the results explainable, but at the same time fail to explain how the raw data is measured or how the AI tool has been utilized. But the benefit of such evidence is that before the defence can argue as to relevancy of evidence and whether such evidence is a part of fact finding, a case narrative has already been prepared via this report. Thus, it then is up to the bench whether to admit such evidence or dismiss it, by taking various means to cross check the report’s findings. It may then promulgate further steps or may order further enquiries, if need be. Keeping AI in Check: What should be scrutinized while accepting AI generated evidence (The Economist, 2018). • Expansiveness of Data base: Since machines learning implies the perennial process of learning, the expansiveness of the training data set must sufficient for a wholistic learning. For instance, while the text recognition- which is a comparatively simple task- may require only a few thousand examples, language translation, being highly complex, would require millions of examples. Thus, a party gunning for admittance of the evidence would want guarantees that the training data is of a sufficient scale, whereas the party seeking to exclude the evidence would inquire after the number of examples the algorithm has has had an opportunity to interactive with and if that number is within the generally accepted standard/procedure for the task. Were the Training Data Gathered or Generated in Ways that Produced a Biased Sample? The data set from which machine learning is initiated, must meet the baseline standard of quality for conclusions to be useful. The conformance to such quality can be easily probed via simple inquiries. For e.g., what must be seen is whether the data received by the researcher (in case of third-party data) can have its authenticity vouched for, or if such vouching possible for data obtained by crowdsourcing and open-source method, some of the common sources of machine learning. Courts are well versed to the shortcomings of data collection methods and as such try to hunt for possible avenues of bias. In State v. Loomis {where the Wisconsin supreme court dealt with the State’s use of Correctional Offender Management Profiling for Alternative Sanctions (“COMPAS”) to determine sentence}, Loomis’s expert stated that the court could not say for sure that the data used by COMPAS was unbiased or even relevant. “The Court does not know how the COMPAS compares that individual’s history with the population that it’s comparing them with. The Court doesn’t even know whether that population is a Wisconsin population,

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a New York population, a California population. . . There’s all kinds of information that the court doesn’t have, and what we’re doing is we’re misinforming the court when we put these graphs in front of them and let them use it for sentence.” Source Code Problems. If the main source programming itself contains errors, then conclusions can easily be biased and not in conformity to “reliable principles and methods” in a programming language • Labelling data by machine: Data Labelling is an essential. It is unlikely that a researcher might label a vast amount of dataset by hand. They may use open datasets (which are open for public viewing), but this lacks reliability, since the origin of such labelling is unknown. Methods like Amazon Mechanical Turk enables any individual to label data for a nominal fee.

Thus, the idea is simple; if one machine can learn labelling, then another machine learning algorithm can make use of such data for further work. Although this strikes a whole debate on the risk adding one machine learning on another. Multi-Faceted Application Of AI (AI in the Courtroom: A Comparative Analysis of Machine Evidence in Criminal Trials, 2020 pp. 195-253) Since AI is multi-dimensional in its approach, it is increasingly being used in myriad areas. Trade secrets and AI evidence. A trade secret is referred to as non-public information that affords reasonable efforts is the subject of reasonable efforts to maintain its secrecy. It confers upon a business an upper hand over their competitors who lack that information. Both data and source code have been consistently seen as trade secrets, so courts have been hesitant to delve deeper into either, even for defendants in criminal actions who could use the information to mount a meaningful defence. Tech firms take special pains to protect trade secrets in machine learning due to the infancy of the field. It means that, established giants have a comparatively lesser advantage as compared to competitive start-ups in areas where they dominate (Like google is the go-to for search engines and Facebook used to be the primary avenue for social media enthusiasts). In an environment devoid of machine learning, parties are unable to decipher the constructions of a program due to its proprietary nature and the programming firms are often disinclined to reveal the source code or data that form the crux of their business success.

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Automated Driving. Automated driving has led to AI keeping human under radar. With increase in tech advancements, humans will give over control to driving assistants, which is being bettered to be more reliable than human skills; so much so, that they already falter less and have significantly less accidents than manual driving. These AI drivers carry out a series of complex actions; monitoring vehicle position, steering patterns, body temperature facial movements, blink rates, posture. On the detection of such anomalies, an alert is issued to driver, instructing him to take a break. In such scenario, the automated driver is the Machine, yet a human must intervene in case the “take over request” becomes essential. We see Germany passing regulations and preparing for fully automated vehicles. Such data is recorded and used later as evidence in court. AI, as of now, is not viewed as something which can stand in a trial, since its intelligence is primarily one dimensional and as a result, is unable to account for its actions and decisions. But various debates by experts stipulate that just because an entity lacks knowledge, it does not absolve it from responsibility. Blame is termed as a social construct and by that logic, it may in time include machines. Thus, they may be made liable and cannot be termed as mere neutral bystander. This would depend on AI’s development as a whole, the focus majorly being at whether it is able assess information fairly accurately, as well as developing some reasoning similar to a human and to the legal concept of “the test of a reasonable man”. It may eventually be seen as secondary witness or suspect (as the case may be) or even a proxy suspect in the case of automated vehicles. Even if it lacks the moral compass or the reasonability, some liability may be attached to. A drowsiness detection system, for instance, can be imprecise or hazy—it may include biased algorithms or standardized data. They may have what is known as automation bias in software designing, which favours corporate self-interest (Regulating Artificial Intelligence Systems: Risks, Challenges, Competencies and Strategies, 2016). Questions as to who owns responsibility when someone is harmed by an autonomous car and how such guilt is to be allocated are closely connected. Substantive Law takes a focused approach on humans as moral agents, actors and criminal risk takers. While the shift to the notion that non-human entities, such as corporations and companies, has been recognized now, prosecutions continue to function within the idea that a human action is subject to criminal liability or a human must be held accountable for a corporation’s misdemeanour, since liability is ascribed to a natural being, who must be denied protection behind the garbs of a manmade entity (this is a common corporate law concept.)

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Case Studies Application of the Convention on the Prevention & Punishment of the Crime of Genocide (Bosnia & Herzegovina V. Serbia & Montenegro) • Dated March 20th, 1993 – the Republic of Bosnia & Herzegovina resorted to the institution of judicial proceedings against the Federal Republic of Yugoslavia with respect to a dispute concerning violations of the Convention on the Prevention & Punishment of the Crime of Genocide, instituted by the United Nations General Assembly on the 9th of December, 1948, along with various matters which Bosnia & Herzegovina claimed were interlinked therewith. The Application invoked Article IX of the aforementioned Convention to form the base for the jurisdiction of the Court. Later on, Bosnia & Herzegovina further invoked additional factual bases for jurisdiction (International Court of Justice). • Immediately after the filing of the petition, Bosnia & Herzegovina submitted a formal request for indication of provisional measures as under Article 41 of the aforementioned stature &, on 1st April, 1993 – Yugoslavia submitted written observations about Bosnia & Herzegovina’s request for the provisional measures, through which they pleaded the Court to order the application of provisional measures. Via an Order dated 8th April, 1993, the Court following the hearing of the parties indicated a few provisional measures with a view favorable to protection of rights under the Genocide Convention (International Court of Justice). • On the 27th of July, 1993, Bosnia & Herzegovina submitted a new Request for the indication of provisional measures & on 10th of August, 1993, Yugoslavia as well submitted a Request for the indication of provisional measures. Via an Order dated 13th of September, 1993, the Court, following pleadings by both the parties reaffirmed the measures earlier indicated & declared that those same measures should be immediately affected. Consequently, within the extended time-limit till 30th of June, 1995 for the filing of a Counter-Memorial, Yugoslavia, referred to Articled 79, Para 1, of the Rules of Court, raising preliminary objections as to both the admissibility of the Application as well as the jurisdiction of the Court to entertain the case (International Court of Justice). • In the Judgment pronounced on the 11th of July, 1996, the Court rejected all preliminary objections raised by the Yugoslavian side & found itself having jurisdiction of dealing with the dispute on the basis of Article IX of the Genocide Convention, while entirely dismissing the additional bases of jurisdiction invoked by the Bosnian & Herzegovinian side. The Court also found that the Convention did bind the two Parties & that there was a legal

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dispute between them which fell within the purview of Article IX (International Court of Justice). • Via an Order dated 23rd of July, 1996, the President of the Court fixed 23rd of July, 1997 to be the time-limit for the filing of a counter-memorial by Yugoslavia on the merits. The counter-memorial was indeed filed within the prescribed time-limit, it requested the Court to adjudge & declare that Bosnia & Herzegovina was responsible for the acts of genocide committed against the Serbs in Bosnia & Herzegovina & also for other violations of the Genocide Convention. The acceptability of the counter-claims under Article 80, para 1, of the Rules of Court were called into question by the Bosnian & Herzegovinian side. The Court ruled on the matter stating that the counterclaims were admissible. The Reply from Bosnia & Herzegovina & the Rejoinder by Yugoslavia were duly filed. In 1999-2000, various exchanges of letters took place in connection to new procedural difficulties, which had emerged in the case. In the April of 2001, Yugoslavia informed the Court of its wish to withdraw its counter-claims, which the President of the Court allowed dated 10th of September, 2001. On the 4th of May, Yugoslavia submitted to the Court a document entitled “Initiative to the Court to reconsider ex officio jurisdiction over Yugoslavia”, in which it contented that the Court firstly had no jurisdiction ratione personae over Serbia & Montenegro, & secondly, requested the Court to “suspend proceedings regarding the merits of the case until a decision on this initiative”. On 1st of July, 2001, they also filed an application for revision of judgment which was found inadmissible. In a letter dated 12th of June, 2003, the Registrar informed the parties to the case of the Court’s decision to not comply with the request for suspension of the proceedings on the merits (International Court of Justice). • Consequent to the public hearings held between 27th of February 2006 & 9th of May, 2006, the Court rendered its Judgment on the merits on 26th of February 2007. It initiated by examining the new jurisdictional issue called into question by the Respondent arising out of its admission into the United Nations as a new member in 2001. The Court confirmed that it had jurisdiction on the basis of Article IX of the Genocide Convention, iterating in particular its 1996 judgment, whereby it had found that it had jurisdiction under the Genocide Convention, advantaged by the “fundamental” principles of res judicata guaranteeing “the stability of legal relations”, & that it was in the best interests of each Party that issues already adjudicated upon in favor of that party is not argued again. The Court went on to make known extensive findings of facts pertaining to the alleged atrocities’ occurrences, & whether they could be characterized as genocide. The Court went on to find that these acts were not in fact committed with the specific intent

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defining a crime of genocide, namely – the intention of destroying wholly or partly, a protected group. Nonetheless, the Court declared that the Republic of Serbia had indeed violated its obligation housed in Article 1 of the Genocide Convention to prevent the Srebrenica genocide. The Court further observed that the obligation required the States that are aware or should normally have been aware, of the grave danger that acts of genocide might potentially be committed, for employing all means reasonably available to them to prevent the said forecasted genocide, well within the limits of international law (International Court of Justice). • The Court went on to further hold that the Respondent had indeed violated its obligation to punish the perpetrators of the genocide, inclusive of failure to cooperate fully with the International Criminal Tribunal for the former Yugoslavia (ICTY) in relation to the handing over of General Ratko Mladic for trial. The failure constituted a violation of the Respondent’s duties demanded under Article VI of the Genocide Convention (International Court of Justice). • With regards to Bosnia & Herzegovina’s request for reparations, the Court held that, owing to the fact that it had not been shown that the genocide at Srebrenica would in fact have been deterred if attempts to prevent it were made; financial compensation for the failure in inhibiting the genocide was not the suited form of reparation. The Court considered that the most appropriate form of satisfactory reparation would be a declaration in the operative clause of the Judgment that Serbia had failed to comply with obligation to prevent the crime of Genocide (International Court of Justice). • With regards to obligations to punish acts of genocide, the Court held that the declaration in the operative clause that Serbia had indeed violated its obligations under the Convention & that it must transfer individuals accused of genocide to the ICTY & must cooperate fully with the Tribunal would constitute appropriate satisfaction (International Court of Justice). The Usage of AI in Detection of Crime – The Risks & Benefits Organizations are continually employing AI mechanisms for the prevention & detection of everything, ranging from routine employee theft to insider trading. Numerous banks & MNCs use AI for prevention & detection of fraud & money laundering. Social media corporations utilize machine learning for blocking illicit content in the like of child pornography. Businesses constantly experiment with newer & innovative ways to utilize AI for better risk management policies & mechanisms in a much more efficient manner including much more responsive fraud detection & even in order to predict & prevent crimes. Although the technology of today is not really revolutionary, the

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algorithms in & the results that can be produced by them are revolutionary (Quest, et al., 2018). Banks have been using transaction monitoring systems based on predefined binary rules for decades; the systems are based upon predefined binary rules that require the output for manual checking. The success rate however, tends to be low – only 2% of the transactions that are flagged by the systems reflect a true crime or mal intent. In contrast to the earlier ones, the machine – learning mechanisms used today utilize predictive rules that can automatically recognize anomalies in data sets. The advanced algorithms of today can significantly cause the reduction of the quantity of false positives by filtering out the cases that were incorrectly, all the while uncovering others that were missed due to the utilization of conventional rules (Quest, et al., 2018). Contingent to the amount of data that is available today, & the incrementing expectations of consumers & public authorities when it comes to the protection & managing of the data, numerous companies have decided on this as one of the only ways for keeping up with the increasingly sophistication of criminals (Quest, et al., 2018). Social media companies are expected to be able to uncover & remove terrorist recruitment videos & messages at moment’s notice. With the passage of time, AI-powered crime-fighting/crime-solving utilities might become requirements for larger businesses, partially owing to the fact that there shall be no other way for the rapid detection & interpretation of patterns across billions of pieces of data (Quest, et al., 2018). One risk as to whether AI crime-fighting solutions might be good strategic fits for companies depends upon the considerations as to whether the benefits outvalue the risks. One risk is that biased conclusions might be drawn by the AI basing upon factors such as ethnicity, gender, & age. Corporations might experience backlash from consumers who stress over the potential misuse or exploitation of their data by even more data-intensive surveillance of the records, communications & transactions – especially if the insights are actually shared among the government(s). For example, a European bank had been forced towards backtracking its plans to ask consumers for the permission for monitoring the social media accounts as parts of the banks’ mortgage application process(es) (Quest, et al., 2018). It is believed that the next stage of AI development shall coincide with the rise of Public-Private Partnerships for AI Crime Prevention. Corporations & Law Enforcement departments are incessantly experimenting AI for improvement of their ability for detection & prevention of crime. They are developing data platforms that they share, reporting protocols & feedback loops. The partnerships towards the fighting of crime shall become increasingly commonplace. Financial organizations, Financial Intelligence

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Units, & law enforcement are initiating such partnerships for sharing data & the usage of AI to detect crime in certain jurisdictions. For example, in the UK, the NSA (National Crime Agency) has been working closely with UK Finance for using AI to be able to identify financial & economic crimes better, & also to cause the improvement in abilities for using financial information for detection of other types of crime such as human trafficking & counterfeiting. Authorities are exploring ways to increase exchange of information & intelligence amongst public & private sectors (Quest, et al., 2018).

Where AI might be used for Detection of Crimes in the Future. AI is commonly used for detection of crimes like fraud & money laundering. However, in the future, it is highly likely that they begin to be used in other industries as well. Three such areas where AI can be used to prevent are : • Transportation of Illegal Goods – Utilizing AI, express delivery companies can effectively assess the probability of parcels containing illicit items, such as narcotics, & reporting them to relevant authorities (Quest, et al., 2018). • Terrorist Activities – The retailers & pharmacies might be able to use sophisticated AI tools for identification of customers who purchase abnormal quantities of chemicals that might be used as precursors to terrorist activities (Quest, et al., 2018). • Human Trafficking - Shipping corporations might be able to use their data & AI capabilities for identification of containers which are most likely to be utilized for human trafficking & thus save lives (Quest, et al., 2018). Corporations are equally interested in the assessment & mitigation of internal risks, which are equally important. Conclusively it can be said that the public private partnerships can potentially put analyse instances of terror funding very effectively, consequently, reducing terrorist activities to a bare minimum, along with other crimes.

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International Law & Internet Governance

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Aditi Sharma and Sameer Samal

Introduction International internet laws can be systematically categorized into two interrelated perspectives, (i) the narrow perspective dealing with the institutions that enable the practical functioning of the internet; and (ii) the wide perspective that refers to the general rules and regulations that intersect and interact with the internet. This chapter is concerned specifically with the latter perspective to elucidate the impact of extant international internet laws on contemporary technologies such as Artificial Intelligence and its subsets. Internet governance has been a subject of state sovereignty right since its inception; therefore, the concepts of data sovereignty and data localization is discussed with the aim of understanding its role in the development of Artificial Intelligence on a global scale. The concerns of cybersecurity and cybercrimes are an invariable part of the internet, and with the advent of advanced AI systems, these concerns only magnify. Hence, this chapter enlists all regional and global initiatives by countries and institutions with a view to add to the existing discourse on the subject. Similar to the rights, whether human rights or general civil rights, enjoyed by individuals throughout the globe in the physical realm, the same set of rights and obligations are also applicable in the digital realm. Therefore, the nuances of international human rights and their conjunction with Artificial Intelligence has been discussed at the latter part of this chapter. In the words of Aristotle, the great Greek philosopher, “man is by nature a social animal; an individual who is unsocial naturally and not accidently is either beneath our notice or more than human. Society is something that precedes the individual”. The human element within an individual is not complete without the realization of the necessary socio-cultural beliefs, and considering the close proximity of Artificial Intelligence systems and the society, this chapter discusses the interaction of AI with individuals and the society from a socio-cultural perspective. In the latter part of the chapter, the authors have discussed the concepts of internet law that align with AI with the assistance of certain contemporary case studies. The five standardized models used to govern the internet depending on the country, and the recent growth of ‘splinternet’ has been discussed.

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Legal Background & Conjunction with Artificial Intelligence Data Sovereignty and Data LocalizationData sovereignty is a relatively established subject in the sphere of international internet governance. Sovereignty is the controlling power exercised by a country within its territory. The control over the cyberspace limited to that country also falls within the sovereign ambit of the country. Therefore, giving birth to certain concepts such as data sovereignty. It can be defined as the absolute control exercised by the sovereign power over the internet space pertaining to the digital existence of its citizens. This aspect of internet governance gained popularity with the discovery of artificial intelligence’s potential. As nations realized the potential AI holds, both economic and social, their contribution towards the discourse regarding data sovereignty increased. Data is considered equivalent to food or fuel for artificial intelligence systems and the more a nation has it, the better. The benefits of data sovereignty that countries have identified include, but are not limited to: • • • • •

better surveillance; increased national security; improved data regulation; better safeguards for citizens; and economic benefits.

The global race towards developing better artificial intelligence systems has given the way for municipal legislations upholding data sovereignty. A nation can successfully maintain its data sovereignty only when the data is available locally, i.e., within its territorial limits. Thus, giving rise to a new concept known as “Data localization”. It is the legal requirement for the data of a citizen to be stored within the territorial limits of that country. This data includes intracountry as well as cross-border transactions. The term ‘transaction’ is used here connotes financial data as well as data generated from non-financial activities. Certain countries have moved a step ahead and have also passed legislations mandating data processing within the country. The benefits that countries hope to achieve are: • improved national security; • economic gain; and • strong safeguards for citizens’ rights.

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Cybersecurity and Cybercrimes Cybersecurity and Cyber Crimes are one of the most important aspects of international internet law but are agreeably the most neglected areas. It is partially because cybercrimes activities are not uniformly defined under international law, but also in part because states have not effectively agreed on the traditional crimes. An ambiguity in the definition of cybercrime activities has resulted in scarce policies on appropriate scale for regulation as well as regularization of cybercrime activities. However, with the advent of advanced technologies such as Artificial Intelligence and its subsets, an urgent need for a secure internet space with sufficient governance is felt. Therefore, to add to the existing discussion, this section will deliberate on; (i) the Budapest Convention on Cybercrime; and (ii) the applicability of international law on cyber conflicts and cyber warfare. Further, the work of certain organisations such as the NATO Cooperative Cyber Defence Centre of Excellence has been briefly discussed. Budapest Convention on Cybercrime The Budapest Convention on Cybercrime is the first international treaty on criminal activities through the internet and other computer networks. The treaty is signed by the member states of the Council of Europe and other States that are additional signatories to it. It particularly deals with issues of copyright infringement, child pornography, computer-related frauds, and network security violations. The purpose of this treaty is to pursue a uniform criminal policy aimed at the protection of society against activities such as cybercrimes. The objective is aimed to be achieved by adopting appropriate legislation and enabling international co-operation (Council of Europe, 2001). The Convention deals with the aforementioned topics under the following sections: • Substantive criminal law that provides for provisions relating to the offences against the confidentiality, integrity and availability of computer data and systems, and other computer and content-related offences, as well as infringements of copyrights and other related rights. • Procedural law that mentions common provisions regarding the scope of procedural provisions and other safeguards. This section also deals with the expedited preservation of stored computer data, real-time collection of computer data, and search and seizure of computer systems. • Jurisdiction over offences mentioned in Articles 2 to 11 of the Convention.

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• General Principles relating to international co-operation such as the principles relating to extradition, mutual assistance between the signatory parties for investigation and other related purposes. An Additional Protocol to the Convention on Cybercrime was adopted on 28th January, 2003 that deals with concerns relating to the criminalisation of acts of a racist and xenophobic nature through computer systems. Applicability of International Law on Cyber Warfare The Tallinn Manual on the International Law Applicable to Cyber Warfare has attempted to define cyber-attack as, ‘a cyber operation, whether offensive or defensive that is reasonably expected to cause injury or death to persons or damage or destruction to objects’ (International Group of Experts, 2013). The initial question to consider before any propositions are introduced in this section is whether the extant international laws are applicable to cyber issues at all, if yes, how. The discussion on this subject varies from a full application of the Law of Armed Conflicts as per the pronouncement of the International Court of Justice that it applies to, ‘any use of force, regardless of the weapons employed’, to a narrower view of the Permanent Court of Justice that acts not forbidden under international law are generally considered to be permitted. Currently the sovereignty, jurisdiction and control over cyberspace is under the purview of those respective States. The uniform principle relating to the sovereign control over cyber infrastructure located in international airspace, high seas, or outer space is usually subject to the jurisdiction of the State of registration. Therefore, the principles of sovereign immunity and inviolability are long-established, and any practice contrary to this principle such as any interference with the cyber infrastructure constitutes a violation of the sovereignty of the respective State. The general principle relating to the prohibition of the use of force under international law includes, but not limited to, that any cyber operation constituting a threat to the political or territorial integrity of a sovereign State is considered unlawful. In response to any cyberattack, a target State may exercise its inherent right of self-defence. However, it is necessary to ensure that the necessity as well as the proportionality of the self-defence must be appropriate (International Group of Experts, 2013). International governmental organizations also play a crucial role in the governance of cybercrimes and cybersecurity issues. The United Nations Charter, under Article 39, empowers the United Nations Security Council to, ‘determine the existence of any threat to the peace, breach of peace, or act of aggression and make recommendations or decide what measures shall be taken

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in accordance with Articles 41 and 42, to maintain or restore international peace and security’. Therefore, it can be safely inferred that, if the UN Security Council determines any cyber operation to be under the purview of the aforementioned provision, it may authorize non-forceful measures as well as forceful measures if deemed necessary. International Cyber Norms The inherent nature of technology has been elucidated by Albert Einstein, although in a pessimistic manner, it is still relevant today. His notion, ‘all our lauded technological progress - our very civilization - is like the axe in the hand of the pathological criminal’, meant that the opportunities that technologies present to mankind also bring certain vulnerabilities with them. The rapid advancement of cyberspace has made it comparatively challenging to regulate and limit malevolent activities (Osula, et al., 2016). Certain prominent multilateral initiatives that limit state activities in cyberspace are: • Organization for Security and Co-operation in Europe (OSCE) and other Confidence-Building Measures. • The Shanghai Cooperation Organization and International Information Security. • The North Atlantic Treaty Organization. • The G20 Antalya Summit. • Measures by the Council of the European Union. The activities concerning the international community’s legal architecture including the cyberspace is dealt in Article 38 of the Statute of the International Court of Justice, which is as follows: “1. The Court, whose function is to decide in accordance with international law such disputes as are submitted to it, shall apply: e. International conventions, whether general or particular, establishing rules expressly recognized by the contesting states; f. international custom, as evidence of a general practice accepted as law; g. the general principles of law recognized by civilized nations; h. subject to the provisions of Article 59, judicial decisions and the teaching of the most highly qualified publicists of the various nations, as subsidiary means for the determination of rules of law. 2. This provision shall not prejudice the power of the Court to decide a case ex aequo et bono, if the parties agree thereto.”

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International legal norms are considered different from the domestic as well as inter-state regulations governing cyberspace under the ground that in the event of non-compliance international legal responsibility and accountability may arise (United Nations, 2001). Traditionally, these international legal norms were agreed to be binding only on the States, the application of which on the domestic organizations of the States were left to the decision of those respective States. Human Rights Human Rights, whether prescribed under international treaties and documents or municipal legislations, are a set of intrinsic and inviolable rights that every human being is inherently born with. These rights are mentioned and outlined under various legal documents at both international as well as domestic levels. The core international human rights instruments are The Universal Declaration of Human Rights, the Charter of United Nations, International Covenant on Civil and Political Rights, International Covenant on Economic, Social and Cultural Rights, Convention against Torture and Other Cruel, Inhuman or Degrading Treatment or Punishment, among others. To cover the aspects of human rights that are related to the use of Artificial Intelligence and its subsets, this section examines the nature of their conjunction under, (i) basic principles of human rights; (ii) civil liberties and freedoms; and (iii) data protection and individual privacy.

Principles of International Human Rights. Human Rights are the inalienable, inviolable, indispensable, and intrinsic rights that every human being, regardless of their sex, race, nationality, language, region, ethnicity or any other protected or unprotected status, is entitled to. These rights include, but not limited to: • • • • • • • • •

The right to life, liberty, and security before courts and just trials. The right to privacy. The right to freedom of movement. The right to freedom of speech and expression, assembly and association. The right to equal treatment and protection against discrimination. The right to political participation. The right to health. The right to adequate standard of living. The right to education.

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This section specifically deals with the conjunction of international human rights with AI. Therefore, the impact of AI on the afore-mentioned core human rights principles will be analysed. • The right to life, liberty, and security before courts and just trials- these rights have been provided under the Universal Declaration of Human Rights and the International Covenant on Civil and Political Rights18.

“Article 3 of the Universal Declaration of Human Rights- Everyone has the right to life, liberty and security of person.” 18

“Article 6 of the Universal Declaration of Human Rights- Everyone has the right to recognition everywhere as a person before the law.” “Article 7 of the Universal Declaration of Human Rights- All are equal before the law and are entitled without any discrimination to equal protection of the law. All are entitled to equal protection against any discrimination in violation of this Declaration and against any incitement to such discrimination.” “Article 8 of the Universal Declaration of Human Rights- Everyone has the right to an effective remedy by the competent national tribunals for acts violating the fundamental rights granted him by the constitution or by law.” “Article 10 of the Universal Declaration of Human Rights- Everyone is entitled in full equality to a fair and public hearing by an independent and impartial tribunal, in the determination of his rights and obligations and of any criminal charge against him.” “Article 6 of the International Covenant on Civil and Political Rights1. Every human being has the inherent right to life. This right shall be protected by law. No one shall be arbitrarily deprived of his life. 2. In countries which have not abolished the death penalty, sentence of death may be imposed only for the most serious crimes in accordance with the law in force at the time of the commission of the crime and not contrary to the provisions of the present Covenant and to the Convention on the Prevention and Punishment of the Crime of Genocide. This penalty can only be carried out pursuant to a final judgement rendered by a competent court. 3. When deprivation of life constitutes the crime of genocide, it is understood that nothing in this article shall authorize any State Party to the present Covenant to derogate in any way from any obligation assumed under the provisions of the Convention on the Prevention and Punishment of the Crime of Genocide. 4. Anyone sentenced to death shall have the right to seek pardon or commutation of the sentence. Amnesty, pardon or commutation of the sentence of death may be granted in all cases. 5. Sentence of death shall not be imposed for crimes committed by persons below eighteen years of age and shall not be carried out on pregnant women. 6. Nothing in this article shall be invoked to delay or to prevent the abolition of capital punishment by any State Party to the present Covenant.”

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“Article 9 of the International Covenant on Civil and Political Rights1. Everyone has the right to liberty and security of person. No one shall be subjected to arbitrary arrest or detention. No one shall be deprived of his liberty except on such grounds and in accordance with such procedure as are established by law. 2. Anyone who is arrested shall be informed, at the time of arrest, of the reasons for his arrest and shall be promptly informed of any charges against him. 3. Anyone arrested or detained on a criminal charge shall be brought promptly before a judge or other officer authorized by law to exercise judicial power and shall be entitled to trial within a reasonable time or to release. It shall not be the general rule that persons awaiting trial shall be detained in custody, but release may be subject to guarantees to appear for trial, at any other stage of the judicial proceedings, and, should occasion arise, for execution of the judgement. 4. Anyone who is deprived of his liberty by arrest or detention shall be entitled to take proceedings before a court, in order that that court may decide without delay on the lawfulness of his detention and order his release if the detention is not lawful. 5. Anyone who has been the victim of unlawful arrest or detention shall have an enforceable right to compensation.” “Article 14 of the International Covenant on Civil and Political Rights1. All persons shall be equal before the courts and tribunals. In the determination of any criminal charge against him, or of his rights and obligations in a suit at law, everyone shall be entitled to a fair and public hearing by a competent, independent and impartial tribunal established by law. The press and the public may be excluded from all or part of a trial for reasons of morals, public order (ordre public) or national security in a democratic society, or when the interest of the private lives of the parties so requires, or to the extent strictly necessary in the opinion of the court in special circumstances where publicity would prejudice the interests of justice; but any judgement rendered in a criminal case or in a suit at law shall be made public except where the interest of juvenile persons otherwise requires or the proceedings concern matrimonial disputes or the guardianship of children. 2. Everyone charged with a criminal offence shall have the right to be presumed innocent until proved guilty according to law. 3. In the determination of any criminal charge against him, everyone shall be entitled to the following minimum guarantees, in full equality: a. To be informed promptly and in detail in a language which he understands of the nature and cause of the charge against him; b. To have adequate time and facilities for the preparation of his defence and to communicate with counsel of his own choosing; c. To be tried without undue delay; d. To be tried in his presence, and to defend himself in person or through legal assistance of his own choosing; to be informed, if he does not have legal assistance, of this right; and to have legal assistance assigned to him, in any case where the interests of justice so require, and without payment by him in any such case if he does not have sufficient means to pay for it; e. To examine, or have examined, the witnesses against him and to obtain the attendance and examination of witnesses on his behalf under the same conditions as witnesses against him; f. To have the free assistance of an interpreter if he cannot understand or speak the language used in court; g. Not to be compelled to testify against himself or to confess guilt.

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As evident from the reproduced provisions of two of the most important instruments of international human rights, the right to life, liberty, security before courts and just trials are all inviolable principles of human rights. The increasing use of Artificial Intelligence technology in the justice delivery system throughout various countries raises serious questions regarding the technology’s interference with the above-mentioned human rights. Certain countries are already facing the bias and issue of racial discrimination in their AI powered criminal justice software (Lindsey Andersen, 2018). Moreover, the Judiciary as well as the Administrative departments of these countries use software provided by third parties such as private service providers. Therefore, a natural lack of technical education and training for government employees and judicial staff is felt. This issue will only worsen in the near future as more advanced as well as complicated technologies are introduced in the judicial as well as administrative departments of countries. • The right to privacy and data protection- the basis of artificial intelligence and its subsets is data analysis, and naturally, the concerns of data protection come annexed to it. It will be futile to expect a precise artificial intelligence system without the possibility of privacy breach, as he who wants a mule without fault, must walk on foot. This section neither intends to discourage the existing discourse regarding artificial intelligence and data protection nor supports the blatant violations of data privacy. It simply aims to identify provisions regarding the right to privacy under international laws and the impact of artificial intelligence upon them. The right to privacy has been declared a human right by the Universal Declaration of Human Rights, the

4. 5. 6.

In the case of juvenile persons, the procedure shall be such as will take account of their age and the desirability of promoting their rehabilitation. Everyone convicted of a crime shall have the right to his conviction and sentence being reviewed by a higher tribunal according to law. When a person has by a final decision been convicted of a criminal offence and when subsequently his conviction has been reversed or he has been pardoned on the ground that a new or newly discovered fact shows conclusively that there has been a miscarriage of justice, the person who has suffered punishment as a result of such conviction shall be compensated according to law, unless it is proved that the non-disclosure of the unknown fact in time is wholly or partly attributable to him. No one shall be liable to be tried or punished again for an offence for which he has already been finally convicted or acquitted in accordance with the law and penal procedure of each country.

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International Covenant on Civil and Political Rights, as well as certain regional instruments19. Privacy, similar to other human rights, is an inalienable right that every individual is inherently born with. The reason this right is considered inalienable is because it cannot be separated from the human personality. The human life and the core human elements are shaped with the sufficient protection of natural and human rights [2]. This right allows humans to exercise their other human and civil rights, and to appropriately realise the potential of their life. The differentiation between secrecy and privacy is an ably discussed subject; therefore, this section will not engage with the same. The realm of privacy can be agreeably spread over the personal and public life of every individual. However, when the thin line differentiating the two fades, it opens the floodgates for a number of privacy-related issues. Artificial Intelligence is based upon data analysis and requires huge quantities of data for training and subsequent decision making. Data is collected to create datasets on which AI and other related algorithmic-systems are trained. This data collection may interfere with data protection and the right to privacy. Considering the increasing application of artificially intelligent systems across fields, the personal as well as public data of individuals is at stake. Commercial activities threaten the personal data of individuals while government sponsored activities, such as identification and monitoring initiatives, threaten the personal and publicly available individual data. Increased digital activity has caused a surge in government surveillance which in turn, affects the right to privacy. To counter such blatant violations of internationally accepted human rights, nations have adopted various regional as well as municipal data protection legislations that aim to regulate this critical subject. • The right to freedom of speech, expression and movement- these rights are the most crucial human rights that an individual can possess to sufficiently realise their societal life to an appropriate extent20. “Article 12 of the Universal Declaration of Human Rights- No one shall be subjected to arbitrary interference with his privacy, family, home or correspondence, nor to attacks upon his honour and reputation. Everyone has the right to the protection of the law against such interference or attacks.” 19

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The potential threat to the above-mentioned rights from artificial intelligence is also from excessive and un-regulated government surveillance. Large amounts of personal data such as location information may be used to interfere with an individual or a group right of free movement. The same data can also be used to predict the group’s movements in the future. As far as the concerns regarding the freedom of speech and expression, almost all nations have stringent provisions that regulate all forms of human expressions for the purpose of national security. With the advent of artificial intelligence and other digital platforms, the notion of expression has also evolved. We have already witnessed various measures adopted by governments throughout the globe attempting to control the content available on digital platforms by either pressurising or using other legal methods.

Socio-cultural Aspects. Networks that connect computers existed way before the internet was introduced in society. However, the facilitation of human communication enumerated creativity, and it supplemented traditional forms of communication with new developments. This made the internet different and hence, the internet was constituted to contain both social and technological facets. Sociocultural issues in Internet Governance are often referred to as “public-policy issues” as they are both public and private, in their own way. (The Politics and Issues of Internet Governance, 2007) They are the broadest and most neglected 2. 3.

Everyone shall be free to leave any country, including his own. The above-mentioned rights shall not be subject to any restrictions except those which are provided by law, are necessary to protect national security, public order (ordre public), public health or morals or the rights and freedoms of others, and are consistent with the other rights recognized in the present Covenant. No one shall be arbitrarily deprived of the right to enter his own country.”

“Article 19 of the International Covenant on Civil and Political Rights1. 2.

Everyone shall have the right to hold opinions without interference. Everyone shall have the right to freedom of expression; this right shall include freedom to seek, receive and impart information and ideas of all kinds, regardless of frontiers, either orally, in writing or in print, in the form of art, or through any other media of his choice. The exercise of the rights provided for in paragraph 2 of this article carries with it special duties and responsibilities. It may therefore be subject to certain restrictions, but these shall only be such as are provided by law and are necessary: a. For respect of the rights or reputations of others; b. For the protection of national security or of public order (ordre public), or of public health or morals.”

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group of issues for many reasons. One of the most appreciated reasons is that they include some very controversial issues, like control over the content, multilingualism, protection of cultural diversity, youth and its education, etc. Digital Identities. In general terms, digital identities mean the identification of people through their digital information. These identifications can be formed in three ways: by the person himself, by a service provider, or issued by the government. Technologies use a person’s biometrics like fingerprints, to create their digital identity. Statistics show that internationally, by 2024, governments will issue about 5 billion digital IDs globally. (VERHULST, et al., 2017) These large-scale national identification programs require algorithms that can match the people’s identity with their credential, store it, and use it. Various middle and lowincome countries lack the required infrastructure and therefore, they have to rely upon private countries to provide this facility. However, every country has its own social and cultural problems relating to issuing digital identities. For example, Estonia has a federated digital identification system, where the country has developed this database, keeping in mind its traditions on privacy protection. Similarly, countries like the UK, Germany, USA, and many others have also developed a system of centralisation of biometric identification of their citizens. Numerous Asian and African countries have also mandated digital identification for the registration of their SIM cards. On the other hand, India has issued biometric and biographical data in the form of Aadhaar, and it is the largest national digital ID system in the world. But, the Supreme Court of India, in 2017 limited the scope and use of Aadhaar due to privacy concerns, despite the contention that digital identity has numerous pros. Similarly, Jamaican courts have also limited the use of any digital identification on the ground that federalisation and compulsory registration of citizen’s identification digitally are beyond the purpose of the government’s identity scheme. (2019) However, data protection and privacy are only concerning that every country advocates. The national legislations deal with these concerns in different ways. Cultural Identity. Cultural diversity is a very wide concept and is not limited to national identities, or traditions, or religions or status. It includes numerous facts within its ambit. There are two views when it comes to cultural diversity and the internet. One view state that the internet is a means through which every country can promote its cultural diversity, globally. It provides an opportunity for individuals in which they can represent themselves in such a way that they represent their national and cultural identities, and hence become a medium of representing and learning various cultures across the globe. Another view states that culture is, in fact, a source of resilience and helps in developing an inclusive information society, whose base is built upon dialogue and mutual respect.

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Diversity of language is the most discussed aspect, in relation to preserving cultural identities through internet usage and its governance. Some statistics show that almost 56 percent of the content on the internet is in English, while almost 75 percent of the population does not speak or read English. (The Politics and Issues of Internet Governance, 2007) This issue has prompted many states to take action and promote multilingualism on the internet, so as to protect cultural identities. The United Nations Educational, Scientific and Cultural Organisation (UNESCO) has taken up numerous initiatives that are based upon promoting multilingualism.

Case Studies The 5 models that govern the internet based on various statesToday, the evolution of the governance method has reached such an extent that it has turned into a complex mechanism, where it has become difficult to standardize the norms. No country can come to a consensus with others, as to who would govern the internet. The debates have formulated five models through which the internet and cyberspace can be governed. (Multistakeholderism: Anatomy of an inchoate Global Institution, 2017) These are: • Cyberspace and Spontaneous Ordering: This model favors that the internet is self-governing and its users require individual liberty, therefore, it must be beyond the control of the government. • Transnational Institutions and International Organizations: The premise of this model is that internet governance transcends national borders, inherently, and hence transnational quasi-private cooperatives or international organizations that are based on treaty arrangements between national governments must have its control. • Code and Internet Architecture: This model is based on the belief that regulatory decisions for the internet are made by the communications protocols along with the other software, therefore, they must determine how the Internet operates. • National Governments and Law: The premises of this model is that importance fundamental regulatory decisions will be taken by national governments through legal regulation. • Market Regulation and Economics: This model states that the market forces drive the fundamental decisions about the nature of the Internet and its regulations.

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Depending upon the geopolitical conditions of a country, different countries have adopted the models accordingly. For example, the United States has adopted the model of Code and Internet Architecture, in which the government has granted almost complete control of the governance to tech companies, and it has minimal control over cyberspace. People have free access to information on the internet. The result of adopting this approach is that presently, the U.S. based tech companies have spread their market across the globe. This has benefitted the countries’ economy immensely. Every country has some tech support of almost every ‘FAAMG’, the biggest five tech giants, and all of them are U.S. based. (Multi-stakeholderism: Anatomy of an inchoate Global Institution, 2017) Various countries adopted a similar approach until China showed its support towards the model of National Governments and Law and started censoring not just its own tech companies but also almost every western tech company. Moreover, because of its large market, it showed a neck and neck competition to the U.S. In India, the government is keen on observing the tech companies’ actions in the country. The decisions of government reflect their acceptance of the Model of National Governments and Law. Incidences like a nation-wide ban on fifty-eight Chinese apps on national security concerns, or the government’s restrictive guidelines for the e-commerce operation of Amazon and Flipkart because they wanted to protect the domestic market by restricting the foreign companies operating in India, proves the same. (E-Governance Policy for Modernizing Government through Digital Democracy in India, 2012) Similarly, China and Russia are strong advocates of “cyberspace sovereignty” and the Model of Cyberspace and Spontaneous Ordering. Splinternet. The term ‘splinternet’ is being increasingly used around the globe in relation to internet governance and regulation. While data localization is a key theme and also the procedural aspect, splinternet is the substantive aspect. It is the fragmentation of the internet on either a regional or national scale for varied reasons. These developments are considered to be highly authoritarian decisions that will only create more disruptions in the global internet platform. Various countries, such as China and Russia have already enacted municipal legislation in support of the fragmentation of their respective internet space and creating a ‘firewall’ to bar outer involvement. The concept of splinternet is a work-in-progress towards a much larger concept known as ‘information sovereignty’. With the advent of advanced technologies such as artificial intelligence and its other subsets that are entirely based on data inputs, information and data sovereignty will prove to be a highly valuable resource for a country, whether economically viable or not.

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These developments will have a huge impact, though not sudden but gradual, on global businesses. With increased restrictions and stringent regulations to promote the notion of splinternet and data sovereignty, it will be more difficult for business enterprises, whether small scale or large scale, to operate in international markets. Countries have been enacting policies regarding internet governance without critically considering its cross-border impacts. An increase in the development of AI powered systems, it is inevitable for countries to consider information and data sovereignty, data localization and the final state of splinternet.

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Section 7: AI & Society

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International Human Rights Law

Abhivardhan and Akash Manwani

Introduction International Human rights are generally meted out in the Universal Declaration of Human Rights (UDHR). The UDHR applies greater emphasis on the core inalienable attributes of these rights. However, for analysis of this piece, more relevant is its differentiation with the legal and other constitutional rights. Legal right is created in favour of the citizenry through a structured system of data collection, research, policy formulation and ultimately passing of the law for one or some specific issues. Basically, these legal rights are specialized which address a specific legal wrong in nature. Legal rights usually come with certain objectives when passed. Same is not the case with human rights which are of general nature and which pose itself as a yardstick for policy formation as well as creation of legal rights as well. Human rights, depending on individual States’ domestic legal structure, are the source of all laws and legal rights have to be created within the rubric of four corners of human rights. Considering that it is the source of all powers, the standard of scrutiny which AI or any technology needs to pass is broad as well as stringent at the same time. The analysis at times would entail exercise through established legal rights as the source of the legal and constitutional rights is human rights.

Legal Background The Algorithmic variable When we articulate our concerns regarding AI, what are we actually concerned about? One could quote a range of issues like privacy possible, discrimination, preferential treatment and so on. Although these are the concerns, wouldn’t it be hypocritical of the human race to worry about existential crises pertaining to AI on one hand and continue producing weapons of mass destruction on the other? Today algorithms decide who gets an interview for a job? What should you eat? How you should travel? Why one should get a loan? Which person you should connect with? AI is certainly more than a mere facilitator. The algorithmic decision-making is taking place at such a deep and broad scale that its influence is felt in our daily movements and surroundings. Research in

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Responsible AI comes closest to regulating the technologies in a viable manner without killing the innovation. Till today, machines have come with a turn off switch. In the reductionist hardware terms, machines are devices, they run on electricity, they are affordable, largely used and dependable. There is no dearth to the potentialities of AI devices. Future machines could get anxious about turning on, they might learn to save energy, self-optimize and could aggressively push towards the object it is made for. This perseverance could bypass human instructions. Personal connection to the technology We are usually fond of, and are possessive about our staple devices. Some of us personify devices by naming and pampering them. These are just presentday devices like laptops, phones and cars. The machines of the future are anticipated to play a much bigger role in the lives of many. Users tend to get more and more passionate and emotional about their devices. This is partly due to the range of personal services which these devices provide nowadays. From facial recognition to voice assistants and self-driving cars, the machines have been playing a greater than expected role in today's time. The newer generation will meet with machines from early ages of their lives. They might look up to machines for feeding their curiosity taking the place of their parents/grandparents. The childhood impact of growing up with mechanical devices could lead a very different kind of generation. To put it technically, how would one feel about our children thinking that technology is just as good and important as actual pets, family or friends (Leung, 2018)? It would be much harder to dispose of a robotic nanny who took care of your child than it is to dispose of a mobile phone or a laptop. This has already been felt, in the year 2007, a U.S Military colonel called off a military mine sweeping exercise because it was inhumane to see robots crawling after losing a leg. When machines play such important roles, when they will be capable of self-optimization, relinquishing autonomy and increasing dependability is not the direction to ponder. One of the more recent examples of machines making inroads into our lives are robotic dogs (SpotLive) developed by Boston Dynamics (Porter, 2021). Apparently, Spot looks like a four-legged apparatus, has a mechanical sound and performs a range of functions. It can be aware of its surroundings, find its way around, put your dishes to wash, slip and fall naturally like living beings and can act playful with its owner. You won’t notice the difference between a real dog and Spot if you cover it with some fur and put on a wagging tail to its back. Like self-charging vacuum cleaners, Spot can also charge itself. Machines

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like Spot would enter the mainstream once the source of juice for the technology is secured. Another example is Sophia (Walsh, 2017), the world famous humanoid which also has citizenship of Saudi Arabia. Reliability and dependability is just one phase of the much bigger horizon while interacting with machines and self learning automated devices. These automated or AI devices could conquer the ability to harness the data, trust and intelligence to achieve its object under any circumstances. If an AI device is made to record surrounding noises, then depending on the algorithmic design, it could configure itself to reduce its hardware functions, save energy and increase the recording capabilities even after switching it off manually. This configuration could also be self-optimized to go against human will and discretion very easily. The bottom line remains, once technology starts playing greater role in lives, we would get more attached to it and lay greater trust in it. It is not entirely an unacceptable or wrong happening, the concern only remains that laying trust rapidly on algorithmic decision making without a system of checks and balances is a step in the wrong direction. Although with such a system as well, algorithmic decision making and extreme blind trust is something to be wary about. Morality & Reasonability Here, the connotation morality enumerates an ability to act in a fair and unbiased manner while the meaning attached to reasonability signifies intelligence. The object of analysis under this heading is whether reasonability automatically triggers morality. This is a direct contradiction between David Hume and Immanuel Kant about the possibilities of rationality/reasonability fixing our value system. Although reasonability would not guarantee morality, it surely sets a pathway in that direction. Rationality is the most important tool in the system of checks and balances. It is one thing to say that the super intelligent being will learn from the data fed into it and another to expect that fair results would come out from biased set of data. Hence, the data being fed to it as well as algorithmic making of the device both need to go through a stringent test of fairness. Apart from this, there must be internal defense mechanisms based on reason for the machines to determine the morality aspects of decision making. Artificial stupidity exists where Artificial intelligence exists. The crux of the same crops from the data which is being fed into the system to get the desired results. In a world where extranet and internet if filled with unverified or fake news, access or outlet to this data could pollute the possibilities of genuine results. Data feeding is again the next stage but at the inceptional stages also, there needs to be an ethical structure. We may not only be under threat from

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robots and self-driving cars, i.e physical threats but also the abstract part of technologies is much more damning. The traceability, tracking, targeted ads, biometric sensors, all have a matter of concern pertaining to privacy as well as possible discrimination. Human Rights & Evolutionary hierarchy Critically noting, there is a Universal Declaration of Human Rights (UDHR) but there is no such declaration for animal rights or any other living beings. There is a clear and objective distinction between intelligent and non-intelligent beings thereby creating natural hierarchical ladders. It is the superior beings (humans) which have been able to control the lives of non-intelligent beings and create a subordinate atmosphere for them to live. The decision on whether a particular land will be developed as a city or as a protected forest will be taken by the intelligent being. In a similar situation, when there would be superintelligent beings which are better than humans then there might arise existential threats for humans. Although looking at present day technological advancement, it is difficult for such an assumption taking place in near future or taking place at all. The kind of relationship we have had with other living beings will be difficult to replicate with AI. The constant fear is that humans won’t be able to create a subordinate environment for AI like they have created for the non-intelligent being. Even if turning it totally around where AI would develop a subordinate role of humans is far from reality, human reliant AI solutions need an ethics cleansing. The only solution to this is a good value alignment exercise wherein from the stage of inception the algorithmic design should have a good AI ethics base. Just like Guiding Principles on Business and Human Rights (United Nations, 2011), a set of principles have been established to integrate human rights into business decisions. Certain basic questions outlined are as follows: "What are the most severe potential impacts?", "Who are the most vulnerable groups?" and "How can we ensure access to remedy?". Human Rights anomalies due to bias The implementation of an AI-like device goes to the core of hampering execution of human rights. There are several ways in which bias can make inroads in an AI device. Bias can make intrusions in mainly two ways viz. system level and data input level. The former system bias takes place due to algorithmic designers personal bias. Ideally, every human suffers from some or the other kind of bias. It is an incorrigible trait of humans who act out of will, necessity, situation, circumstances and biases. These aspects play a key role despite there

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being clear instructions and guidelines. At an objective level, such a presumption about each and every human being cannot be made but there needs to be a system of checks and balances which shall ensure that such bias does not make intrusions into the systems and technologies. Formulating, enhancing and regulating this structure of checks and balances is also known as AI ethics. The later method by which bias could take place is when data is inputted into the machine. It is a matter of common sense that whatever examples an AI device is fed with is going to produce results of similar nature. For instance, the process of credit scoring a person. Credit scores of a person would usually depend on traditional financial documents like income statements, salary receipts, spendings etc. In a situation where alternative sources of credit scoring is adopted, things get a little bit complicated. What if banks rely on one’s online accessible location and determine locality to measure credit score? What if a list of facebook friends is used to make decisions with respect to this (Andersen, 2019)? This can be rectified with reconfiguration of the measuring parameters but the results itself will be biased if the algorithmic design is biased. Applying AI to the existing framework of laws One of the questions which arises is why and when the existing laws governing other technologies become redundant in case of AI. It is not that case that AI is some magical, unknown being over which humans have no control. The existing set of laws already lay down important general rules pertaining to good faith, rights and conscience. The general nature of the principles based on which these laws are formed are encompassing and have accommodated most of the scenarios till date. For instance, any new AI device will have to stand the test of the right against discrimination. It is not the case that a new parallel will be drawn because a new disruptive technology has entered the stream. While AI is not particularly exceptional, it just creates new modes and methods of plausible oppressions (Veen, 2018). Detection and implementation of wellestablished human rights principles is difficult in AI technology. The AI functionalities take place backstage and are very abstract. One could, only from the results of such determination seek to mete out whether discrimination or violation of any other human rights has taken place. Human rights on the other hand gives moral legitimacy to performing functions. Violations of human rights have greater implications and are attached to political and reputational costs. It attracts international attention and shaming the violators have acted as an effective tool. Human rights are usually of expansive and general nature hence potential compartments where AI can fit into the established system of human rights needs to be explored instead of making new verticals.

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Conjunction with AI The relationship with AI and human rights has to be estimated on a fundamental basis to ensure that a credible analysis on the genesis of human rights and AI as a disruptive technology is made possible. This section covers the conjunction of international human rights law with artificial intelligence (especially AI Ethics as a soft law) on the following commonalities that form the conjunction: • The Elementary Relationship with AI & Human Rights • AI’s Treatment of the Fungible Metamorphosis of Human Rights in Jurisprudence • AI’s Role and Status in International Human Rights Law • AI & Human Privity

To deal with subsection, we will take the following kinds of classification for the purpose of evaluation: • Subject-Object-Third Party (SOTP) Classification • Concept-Entity-Industry (CEI) Classification The Elementary Relationship with AI & Human Rights Human rights in the field of law, in general, is beyond the three-tier classification of the generations as was earlier made by Karel Vasak, a Czech jurist (Human Rights: A Thirty-Year Struggle: the Sustained Efforts to give Force of law to the Universal Declaration of Human Rights, 1977). The notion of human rights has become way expansive and all-comprehensive, which is how we generally usher it in terms of interpreting the safeguarding and regulation of freedoms. Practically, we have to establish the relationship between AI Ethics as a Soft Law and International Human Rights Law. Let us see the possibilities of the elementary relationship on the basis of the 2 models of classification: As per the SOTP Classification proposed: • AI can resemble itself as a Subject to any activity or operation in an environment. The relationship between AI and human rights law establishes when the effect of AI being a subject is characteristically involved in the emanative cause to enforce, adjudicate, maintain or recognize a human right.

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If there is no emanative cause directed at humans as objects, establishing the relationship would be unreasonable; • AI can resemble itself as an Object to any subject, like humans. GDPR recognizes the rights of the data subjects, for example (humans), and so forth there should not be any generic problem in establishing any AI-human rights relationship. There is ongoing research on such a relationship already; • AI as a Third Party is an interesting uncharted legal territory. When we recognize AI as a third party, in human rights law enforcement issues, we have to recognize the fact that this correlation is quite inexplicable if the entitative status of AI is not clarified. Although, in the interpretation of AI being an entity, there is no disagreement in accepting that there can be two possible notions: either AI is an electronic legal personality (European Parliament, 2020 pp. 34-36), for example or it can be under some form of possible agency, thereby establishing the legal formula of corporal liability where liability stands over the developers, manufacturers, company executives etc., who exist under the clout of principal of the AI agent. However, while the former case is uncharted, and the latter case may not, let us be clear that as a third party, AI’s treatment as a legal/juristic personality has to be personified in some reasonable manner to establish the relationship between AI and human rights law. It can also be argued that the third-party scenario is a middle scenario between AI being a Subject and AI being an object; However, in either of the 3 examples, the subject-matter, which is AI, will be essentially important because even if on principle, a technical relationship can be established, in practice cum experience, it would not be possible to do the same unless we are clear with what kind of AI is being utilized. So, AI is again conceptually abstract despite having its different definitions and concepts. Also, there are different kinds of products and services, where AI can be present or manifestly available either as a Subject, an Object or that manifest availability is convincing enough to prove that AI resembles or at least vicariously or principally represents itself as a Third Party. Therefore, you need that SOTP classification initially to test the manifest availability of AI (you can do it through analyzing the systemic features of the product/service simply or the ML project), which is then followed by a generic legal interpretation to decide it would be a Subject/an Object/a Third Party (meaning using the SOTP classification again to decide the legal recourse of the AI as a legal/juristic entity). Let us understand why the idea of ‘manifest availability’ is important. Since AI is conceptually abstract, it is important to understand that in practice, algorithmic activities render different tools which can be considered as AI. Even classifying AI into different kinds of products and services would be reasonable, considering the industrial needs and schematic construction behind

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the products and services (European Parliament, 2020 pp. 41-43). This therefore enables diversification of the manifest availability of AI. However, it depends if that manifest availability duplicates in a legal sense or not (for example, the schematic and scientific sources show that the AI in any form possible used is a subject, but in legal interpretation of human rights law, it is sought that the AI behaves to be a third party). Under the CEI classification proposed:

• AI as a concept is suggested to be interpreted very limitedly, because erroneous and abstract binary interpretations, which transcend the fields of law, technology and social sciences in an unrealistic manner, would invite a self-defeat of the need to regulate AI via civil/criminal liability21 framework. In the context of human rights, if the manifest availability as discussed previously is tested, then at a conceptual level for the purpose of legislative work or adjudication, AI as a concept can be indeed well-represented and discussed with an improved and credible model of understanding; • As an entity, AI would be recognized very clearly as per the SOTP framework, initially so that being either legal or juristic in status, some preliminary cause is established; • As an industry, as a follow-up to the SOTP classification, how practically various sectors (consumer services, health, entertainment, etc.,) work, and manifestly involve AI after its entity-based ascertainment, would critically involve some risk assessment into the environment, the data points and even the interactions between human and AI in the best manner possible – which would surely help in ascertaining a case-by-case approach of rendering risk assessment of AI (European Parliament, 2020); AI’s Treatment of the Fungible Metamorphosis of Human Rights in Jurisprudence This subsection is a critical analysis of how human rights law is generally put into the picture and usurped with the rise of disruptive technology. Now, a general practice has been seen commonly, especially in the United States (National Security Commission on Artificial Intelligence, the US Government, 21

One of the interesting examples is the European Parliament’s recent 2020 work on AI and Civil Liability (European Parliament, 2020 pp. 12-19, 34-36, 41-43). The work is very concise and specific in its deliberate interpretations over AI Ethics and Civil Liability, and expresses contrarian points by scholars and policy researchers on even defining AI as an electronic legal personality, showing that even the author and the committee care about avoiding intangible and erroneous interpretations in technology law, over AI as a concept.

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2020 pp. 42, 44, 76-77; Zegart, et al., 2019; Kris, 2017), UAE (Bjola, 2020), China (Business Today, 2020; Kubota, 2019; Roberts, et al., 2020) & the Council of Europe (Council of Europe, 2018), that a special emphasis has been given to algorithmic activities and operations, which translates from diplomacy to economics to national security.

Fungibility in Human Rights Jurisprudence. The concept of human rights is fungible, and all-expansive. The extensibility of the concepts and notions of human rights through making the intangible as tangible (Balkin, 2011 p. 139; Douzinas, 2019 pp. 89-90, 92, 102), usually, is what the real subject-matter of this sub-section. It is important to understand that the ends of human rights is achieve freedom. However, the recent trends of expanding notions of human rights everywhere definitely problematizes any subject-matter possible. AI is not much far-away from it either. Now, the best way to establish how AI treats the fungible transformation of human rights in the field of jurisprudence is by recognizing the role of the following factors: • Data • Algorithms and their activities/operations • How AI Ethics is Adjudicated In all these factors, focus will always be on a human’s privity, i.e., how these factors affect human dignity both at an individual level and at the level of the collective, in the direction towards a direct scrutiny of the theories and practice of state, community and global consequentialism (since the approach of technology law in data, ethics and algorithmic studies is majorly risk-based (Information Commissioner's Office, UK; Council of Europe, 2018; Thales, 2019)).

Data. Data regulation and regularization is important. Before even the question of algorithmic sovereignty (involving the notion of sovereignty over black-box AI algorithms and their inexplicability), data sovereignty is an important and emerging characteristic of international cyber law. European Union’s GDPR establishes extraterritorial jurisdiction already through an addendum in the regulation if the data subjects are found to be EU citizens, for example. Data localization, cloud storage, privacy protection, encryption etc., are the procedural and substantive aspects behind the role of data as a factor in AI and human rights jurisprudence. Data is not the new oil, for sure, but it is important to understand the information/content, which data manifests. Much of it in the context of cultural heritage, tangible & intangible, is dealt in the chapter on international cultural law.

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Besides this, here are some important principles and concepts with regards to data and human rights, which are essential enough to establish auditing/scrutiny/explicability and risk assessment of the relationship between AI and human rights, which are enumerated as follows: Data Quality. • It involves protecting and investing to ensure that the quality of information/content received (for e.g., Art. 13 of the GDPR), is fair, updated and credible. Most of the time, its related to auditing, but in this content, data quality can effectively contribute towards ensuring that the human privity & privacy are inherently preserved and protected; • Data quality as a concept is also involved in the foot-printing of human identity (or digital foot-printing so we say). Digital foot-printing, its history, maintenance and its trends are essential for any forensic/intelligence/risk assessment to estimate the congenial and foreseeable impact AI can probably have upon the mandate and principled responsibility of the state to ensure that human rights are safeguarded at the first place; Data Subject.

• Here, companies, governments, NGOs and even associations can be data subjects (for example Art. 4(1) of the GDPR). However, in the context of the chapter, the reference will purely be on humans as data subjects. Since the SOTP and CEI classifications on AI and its relationship with human rights has been discussed, then in a follow-up, it can be suggested that companies and entities, who are interested to make AI manifestly available in the best tangible means possible, would be surely targeting to subject humans with it as the case goes in; • Therefore, the essence of a human data subject lies in the treatment that it is subjected to. The concept here also gives a preliminary basis to render risk assessments in the lines of the expansive and extensive nature of human rights jurisprudence. However, conceptually, the balance between the moral responsibility of the state to preserve human dignity & the ethical implications to adjudicate the rights and grievances of the data subject has to be struck; • Since a human being a data subject would have some data protection rights, which definitely might or might not be human rights, it is essential that the customary behaviour of human rights recognition and enforcement, which differs among countries despite the universality of international human rights law is not ignored (Council of Europe Parliamentary Assembly, 2015 pp. 1, ¶ 4, 10); Data Anonymization & Pseudonymization.

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• Pseudonymization is an ‘alter-ego’ of anonymization, where (for e.g., Art. 4(5), GDPR) it becomes legally important that any activity involving sensitive data stored is generally required to be stored in such a manner that without any additional information, the data stored is not attributable to the data subject from whom the data has been received. Interestingly, anonymization seems different because the idea focuses on anonymizing data after all. Pseudonymization is just a reverse-engineered legal idea per se, creating a backdrop of liability cum accountability ab initio; • Generally, the role of the conception could be limited to normal data collection and storage activities, but here, while human rights jurisprudence does not have much clear role, because the concept focuses on a clear and tangible enforcement of legal rights, the principle of Privacy by Design (& Default) would surely enable a clear cause for human rights law interpretation because: ─ In the case of artificial intelligence-based products and services, the dynamic and obscure characteristics of AI, which are manifestly available would surely be reflective when it would be required for the principal/company to prove that effective measures under the same principle were taken to ensure that privacy by default and design is systemically safer; ─ Data storage would never be a human rights issue; it is purely a legal concern. However, the systemic congeniality of the pseudonymization infrastructure is very important here, and it cannot be ignored. Other than the fact that AI is dynamic and is manifestly available, the technological semblance between the algorithms (and their operations/activities) & the pseudonymized infrastructure would be tested;

Algorithms and their Activities/Operations. Algorithms can be governed by/held accountable through data sovereignty policies, and also through various protectionist/non-protectionist data policies, where customary international law would play a very important role, through magnifying the critical role of state practices and also adjudicating the sovereign character of states22. There are various concepts in the field of AI Ethics (as a soft law), which cover the role of algorithms, to ensure liability is established. Let us discuss some of the important conceptions of AI Ethics in connection with how algorithms would be treated in human rights jurisprudence. Algorithmic Accountability & Responsible AI.

Please refer to Chapter 2 for more information.

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• Algorithms must be assessed on the basis of the life-cycle of the AI product/service and the activities/operations they undergo. A robust legal framework of algorithmic accountability is therefore important. The concept of Responsible AI on the other hand is helpful because it defines how companies/private entities/trusts/NGOs/other entities and governments would shape the notion of responsibility, accountability and liability, thereby very importantly contributing in the notions of customary international human rights law on the effects of algorithmic activities and operations on the human rights of data subjects; Algorithmic Transparency & Explainable AI.

• The idea of Explainable AI or XAI is essential because explicability of disruptive technology in the field of human rights does not limit itself with the legal obligation to be transparent, but also extends to reflect how algorithmic operations/activities attribute their effects on the human data subject. A take could be that the fault lines of an AI proved by its explicability could show the systemic biases within the system. However, the take is of no use in consideration because biases, human and algorithmic, are natural and strategic. It is therefore more of a policy and enforcement issue, and not a legalistic issue, where subjective or abstract interpretations on the relationship between AI and human rights can be easily established. XAI – if leads to algorithmic transparency would then invite more anthropological study on the way any manifestly available AI talks to human data subject through the genealogy and quality of data & the privacy of design (and default) that it possesses; Algorithmic Discrimination & Fairness in AI.

• Discrimination is a complex legal issue, and is clearly a tarnishing damage to human identity, originality and dignity. The legal basis of algorithmic discrimination can be moral (because of procedural regularity which reflects similar trends of discrimination), ethical (since discrimination is an ill, which has to be avoided ab initio) & then self-replenishing (means that discrimination should not institutionalize itself in any possible foot-print). Now, since biases are natural and strategic, it is therefore imminent that the root basis of algorithmic ethics & the notions cum assumptions of algorithmic operations/activities would surely be challenged. Therefore, it must be proportionately dealt in both legal and policy ways. Discrimination is always alleviated, avoided and moderated. However, considering the SOTP and CEI classifications, it has to be seen how the manifest availability of AI causes discrimination, and how it is dealt reasonably. Mere legal action

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would not help, and more incremental anthropomorphic research would be needed; AI’s Role and Status in International Human Rights Law Adding up with the SOTP and CEI classifications, the role of AI in international human rights law will be more tested on conceptual and entitative grounds, rather than industrial/sectorial grounds, because diplomacy and confidence-building would follow to ensure that state practices, the following customary international legal practices, etc., are properly lubricated, either through international organizations and summits, or through plurilateral activities and relations. The role of human rights bodies would certainly be essential, but it is clear that sovereignty would shape the security and legal apparatus of AI Ethics in IHRL (Council of Europe Parliamentary Assembly, 2015; European Parliament, 2020). There is existent literature already which emphasizes on the relationship between data and algorithms, thereby giving leverage to data protection law & law on cyber operations as well in ensuring that different subordinate fields of international law, such as international privacy law, international telecommunication law & international cyber law are mutually attributive and distributive to each other23. Let us evaluate the scope and fungibility of some common human rights principles enshrined commonly in various human rights treaties and declarations.

Right to Life, Liberty, Security & Dignity. • Recognizing right to life has a transient coherence with adjudicating and holding the state to account for how state interests regulate and influence the public facets of human life. Privacy, environmental challenges, security, rights against self-incrimination, etc., have been widely interpreted in due connection with the idea of right to life; • In the context of AI Ethics, the right to life will be emanated through the theory of ‘cluster of rights’, where a sense of possibility seems that the conceptions of liberty, security (cyber and physical (personal & interpersonal)) and individual dignity would be importantly recognized for the purpose of risk assessment and avoidance measures. • While right to life is one of the few inalienable freedoms, which has to be preserved despite the prevalence of emergency conditions/armed conflicts under international law, it would be contentious and relevant to check, as how will the extension of right to life is regulated. In the domain of security, 23

One example can be cited from Rules 37 and 38 of the Tallinn Manual 2.0 (Schmitt, 2017 pp. 190, ¶ 9; 192, ¶ 13-14; 194, ¶ 20-21).

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a state has to focus on protecting its people, in both cyber and physical domains. Liberty is at the heart of human rights, which itself has to be understood through some anthropomorphic context, because the cluster of rights, when is recognized and revered, is always accepted within a human undertaking or understanding of things and subject-matters. Therefore, a concept of machinic empathy (Modeling empathy: building a link between affective and cognitive processes, 2020; Do Emotions Matter in the Ethics of Human-Robot Interaction?, 2014) also comes into play, which is important for anyone to understand how the ontological words of human and AI environments talk to each other and converse; • We can also interpret this in the following way: imparting a limited form of intelligence (since for example the term robot has its origins to the world slav, meaning slaves, hence limiting the aesthetic scope of robots for industrial work or work ‘equivalent to that of workers’) has a cultural and knowledge sharing aspect too, which in turn affects the way we understand right to life. Therefore, adding to the point of machinic empathy, it is clear that this form of cultural congeniality might be tested as well;

Freedom of Speech and Expression. • Freedom of Speech and Expression is indeed the most basic and extremely correlative aspect of international human rights law, which must not be seen just through an activist lens (Douzinas, 2019). Expression enables creativity, feedback and conversational-collaborative governance. Considering the lack of machinic empathy, which can exist in AI products and services, and the chances of algorithmic discrimination that can happen (President of the United States, 2020; Yaraghi, 2018), it has to be established clearly that the regulation of hate speech, and even its classification, should be limited within the scope of the state (YouTube, 2021; Lapin, 2021)24. For example, social media platforms are becoming public utility platforms (if not all, then at least platforms led by big tech companies, for example, Google, Facebook, Twitter, etc.,), which owes to the coherent need of cyberspace for the basic necessities of people. It therefore is important that the freedom of expression under international human rights law is protected and respected. At the same time, it is essential that the regulation of free speech is vested with the states and not companies. One of the simple reasons is that multinational big tech companies also have their monopoly over social media services around different countries, and if they do not comply with states 24

The example regarding the Ugandan Government’s banning of social media apps before the election is a recent case, and cannot be regarded as an ideal or perfect way of attaining state control on hate speech moderation practices etc., during the elections.

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laws, like Twitter for example had to accept Singapore’s considerations (Romero, 2021), and TikTok had to rescind its services in India at the ban of several Chinese Apps by the Indian Government (Dhapola, 2020). It is therefore important that freedom of expression is reasonably upheld and regulated without endorsing any negative regulations, to ensure that the commitments of international human rights are adhered by companies and governments around the world;

Choice Rights. • The factor of machinic empathy, adding the aspect of SOTP classification of AI products and services, would affect choice rights in international law. Since establishing liability on algorithmic operations/activities requires reasonable state intervention, it is important to establish that the different intersectional and transpiring activities emerge due to the human rights which are exercised by human data subjects, taking into consideration the omnipresent and all-cohesive behaviour of disruptive technologies25, there is no doubt that the finite and basic aspects behind the exercised and recognized human rights (or constitutional rights in a national context) must be protected. Additionally, the intersectional and transpiring activities generally enable people to exercise to have a right to choose; • Now, choice rights, can be identitarian, or proceduralist, or substantive, based on the kind of preferences (legal), which are available. Choice rights can be available in any possible scenario: (1) economics when it comes to buying products and taking services (reference to anti-trust law issues); (2) political freedoms with privity and privacy against surveillance and political correctness assumed by third parties; (3) creative freedoms against cyber contamination and shadow bans; and many more;

Political Freedoms. • Preserving the political freedoms of individuals and non-state actors (legal and legitimate) is hard often, whether a system is democratic or not. AI does not understand the facets and strategic biases which leads to the emanation and transformation of political worldviews and ideologies. Generally, it is a habit that most democracies, for example are distinguished within the leftright model of politics (originated in the French Revolution of 1789). However, business interests should never intrude into the political spheres of human life, while the political interests by the state or political/pressure

Please refer to Chapter 1 on Disruptive Technologies and also the Chapter on International Law and Emerging Technologies.

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groups/people should never interfere into business and entrepreneurship activities; • Both the US and China for example have failed in separating business and politics, thereby failing to protect the political freedoms of their people. The US lacks a clear policy against unreasonable algorithmic censorship (President of the United States, 2020), while China has been accused of purging businesses and activists due to political interests (Nakazawa, 2020). It is essential that in order to ensure that the civil and political rights of individuals and private actors are protected, states must be held responsible to ensure fairer political freedoms to people. However, in the case of algorithmic/AI involvement into the political sphere of people around the world, the left-right model of politics cannot be attributed into various countries, because of the inherent biases in the model of politics to be too much West-centric. Therefore, the design of political freedoms within AI supervision has to be multipolar and diverse; AI & Human Privity Human Privity is not human privacy: it means that any human being deserves some structural and procedural privity in a social environment. As discussed in the previous sub-sections, AI’s SOTP classification and estimating machinic empathy are essential to ensure that the human-led standards of international human rights law are preserved, protected and respected. Human privity can also be connected with human dignity, wherein the difference between the both of them would be that while the source of human dignity is moral, the source of human privity is ethical, realistic and in line with the all-comprehensive and fungible nature of human rights law. In relation with AI Ethics, here are the enumerated corollaries on how AI influences and is a concomitant of human privity: • Let us consider under the SOTP classification, considering AI as an entity (legal/juristic) as a concomitant, which is foreign to human empathy. Despite the fact that it can crush, digest and process the data, which can be schematically scrutinized/monitored, it can become an issue that the inherent autonomy that a human individual should possess is not respected or is unfortunately, infringed. This is not similar to privacy infringement, because privacy infringement is defined, finite and concerted. Infringement of human privity is transitory and transpiring; • This can be found in cases related to digital colonialism (White, 2001; Luskin, 2012; McPhail, 2014; Sutcliffe, 2019), private censorship (President of the United States, 2020; Yaraghi, 2018; Council of Europe Parliamentary Assembly, 2015) & algorithmic discrimination predominantly;

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• Protecting human privity is not just a philosophical form of legal inquiry in constitutional law, but is a technical means to ensure that liberties are preserved in a credible & reasonable manner. Ensuring that is directly connected with how culturally it is driven, which will be specifically dealt in the chapter on international cultural law in detail;

Case Studies Digital Authoritarianism: China, AI & Human Rights China offers a unique sample for AI & human rights analysis. In real time, it offers a peek at how technologies could interact with humans in a minimal human rights environment and what are its implications. Most certainly, sophistication of surveillance tools is mastered by China. State authoritative regime has helped develop and implement AI to track traffic offenders, implement instant fines and collect mass public data for quick prosecution. The greater problem which arises due to this is that China is continually perfecting its technologies which will eventually bring down the crime rate and increase the compliance rate. Although this is public good, it comes at a cost. With such exemplary powers, the general population of the country is under constant surveillance of the state which shall not only affect one’s personal autonomy but also privacy rights and choices. Along with privacy concerns, people would generally be more obedient towards the government which hampers dissent (Sahin, 2020). As per the AI Global Surveillance (AIGS) Index, China provides AI surveillance technologies to 63 countries and out of these countries, 36 countries come within the ambit of Belt Road Initiative (Feldstein, 2019). President Xi had stated that “we would like to be the cyber superpower and use this to spread positive information, uphold the correct political director and guide public opinion and values towards the right direction” (Rogier Creemers, 2018). This goes to the core of the right to self-determination of a mass population of the world. The state with the help of algorithms tracks your preferences, activities, purchases, what you write, share, subscribe to, and then ultimately makes a decision on the level of your “mental cleansing”. Ironically, China gives us a sneak peek into what would be our lives and how would be our lives without privacy regulations and AI ethics in place.

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The Toronto Declaration: Right to equality and non-discriminations in machine learning systems & its effectiveness In 2018, Amnesty International, Access Now alongwith some other partner organization launched The Toronto Declaration. Although concrete steps to implement the objects of the declarations have not been taken, it was the first initiative to draw a convergence between international human rights law and artificial intelligence. The object of this Conference was to enhance the right to equality and non-discriminations in machine learning systems. More specifically, it aims to have a structured system to avoid deeply biased data to be fed into machine learning systems. The framework of the declaration is to promote the right to equality and nondiscrimination, prevention of discrimination and protecting the rights of individuals and groups: promoting diversity and inclusion. The duties of the States as per the Declaration are to formulate standards when it comes to State use of machine learning systems, promoting equality and holding the private sector accountable. It also lays down that the duties for private sector entities and a framework for human rights due diligence before implementing potentially hazardous machine learning systems. All this is of no use without a system for implementation hence the declaration has announced the right to effective remedy. It enumerates that access to justice is fundamental to international human rights law whereby it motivates the private and state entities to have a transparent, quick and meaningful system of redress in case of human rights violations due to machine learning systems or artificial intelligence (The Toronto Declaration, 2018). Unboxing Artificial Intelligence: 10 steps to protect Human Rights by the Council of Europe Realizing the necessity for protecting international human rights, setting standards and implementing redress mechanism for victims of algorithmic bias, a framework of 10 points was formulated by the Council of Europe as follows (Unboxing Artificial Intelligence: 10 steps to protect Human Rights, May, 2019): • Human Rights Impact Assessment – Member States are required to identify such technologies and undertake a self-assessment exercise to delineate plausible breaches of human rights in existing use of technologies by the public authorities. • Public Consultations – The Member States should ensure that timely and prior publications pertaining to implementation of AI systems should be

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undertaken. After such public notifications, viewpoints should be invited and processed in a transparent manner. Obligations of Member States to facilitate the implementation of human rights standards in the private sector – Member States should ensure that all the AI actors i.e creators, owners, managers, manufacturers and service providers will be bound by the United Nations Guiding Principles on Business & Human Rights. Information and Transparency – The implemented AI systems should not only be made public but also should be in a manner that the general public understands clearly. Independent oversight – The Member States should make provisions for a legislative framework to incorporate judicial, quasi-judicial and parliamentary oversight bodies. Non-discrimination and equality – The Member States should unanimously ensure that all the communities and people in general are imbibed in an AI environment without any hint of discrimination. Data protection and privacy – The development of AI systems depend on the input of data and processing. While it is important to continue the development of AI, it must be ensured that the same is undertaken for a legitimate purpose and attempts shall be made to strike a balance between the welfare derived from data collection and contrast it with protection of privacy rights. Freedom of expression, freedom of assembly and association and the right to work – Member States should promote pluralistic and diverse views without shunning any group. Remedies – Under all circumstances, an AI device must remain under human control. Even if the AI system includes a functionality of decision making, there should be a structure for oversight bodies. Promotion of AI literacy – Appropriate steps must be taken to ensure that misinformation on AI is reduced and right knowledge and education is imparted pertaining to real scope of AI.

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International Cultural Law

Abhivardhan & Akash Manwani

Introduction International Cultural Law makes the fragmentation in international legal order very apparent. International law is already complex due to its implementation, multi-layered branches of law, proliferation of domestic tribunals and plausible conflicts in jurisdictional issues. Every country has a distinct cultural heritage which is often in conflict with international legal order. On account of conflicts and otherwise, domestic cultural heritage has resulted in setting up of several of norms and actors in international law. Several domestic cultural groups operate within a state based on community, race, religion, business, tribe, social work and so on. These groups then go on to receive international recognition in the form of minorities, NGOs, indigenous people and communities. These non-state actors have an international standing without governmental borders which then contribute culturally pluralistic viewpoints. Right to Culture, although not expressly a human right, is covered under right to dignity. Hence, structures of cultural importance like libraries and place of worship are protected from demolition. At times these structures gain so much importance that international protection of the same is seek despite of domestic States’ defiance. International legal order has been taking culture the wrong way or has less understanding of the same. Each culture comes with a different specification for the mode of life pertaining to conducting business, managing family and performing rites and rituals. There is no doubt that subjugation of rights in the name of culture is un-compromisable. At the same time laterally transplanting democratization of culturally distinct functionalities, by force and in the name of goodwill is in fact dematerializing the crux of culture.

Legal Background Earlier, intangible cultural aspects like folklore, musical expression, literacy, dance, theatre, poetry, agricultural practices, medicinal practices, skill/art as enumerated by the UNESCO Recommendation on the Safeguarding of Traditional Culture and Folklore (1989) (Recommendation on the Safeguarding of Traditional Culture and Folklore, 1989) was the standard of measurement. Despite of adoption of recognition of above-mentioned contours of culture in

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the recommendation, several States simply did not follow the guidelines, set up authorities or took steps to protect the culture. Then reassessments through meetings took place and adoption of Guidelines on Living Human Treasures (Guidelines on Living Human Treasures, 1994) was adopted in the year 1994. In the year 2003 another initiative Masterpieces Proclamation Programme and Safeguarding Strategies for Intangible Cultural Heritage (Masterpieces Proclamation Programme and Safeguarding Strategies for Intangible Cultural Heritage, 2003) which concentrated on recognition and listing of good forms and traditional culture. With these initial concepts in mind, the definition of intangible culture was rethought and it was that more involvement in actors preserving the folklore should be included in the definition of. Hence as per a Conference on Global Assessment of the 1989 Recommendation on the Safeguarding of Traditional Culture and Folklore: Local Empowerment and International Cooperation (Global Assessment on the 1989 Recommendation on the Safeguarding of Traditional Culture and Folklore, 2001), it was reiterated that alongwith protections to folklore and popular culture, in the center of the protection shield should be people, communities and groups who maintain this intangible cultural heritage. A new Convention on Safeguarding the Intangible Cultural Heritage (Convention for the Safeguarding of the Intangible Cultural Heritage, 2003) was adopted to protect and safeguard the skills, knowledge, expressions and representations beyond the national interests. The rationale of this Convention is that apart from national interests, this cultural heritage which is recreated generations after generations sometimes have international significance spanning across national contours (Beyond State Sovereignty: The Protection of Cultural Heritage as a Shared Interest of Humanity, 2004). Artificial Intelligence and Irreversibility An ideal Artificial Intelligence device should constitute morality and reasonability. AI ethics norms which are constantly in formulation attend to need of morality in AI. Reasonability is gained on the basis of algorithmic design and quality of structured data which is fed in. The bottom-line object of robust machine learning or weak-AI is to identify patterns in the data sets and replicate with algorithmic tweaks to address individual cases. There are numerous algorithms which are most common in use nowadays viz. regression, clustering, Bayesian, instance-based algorithms, deep learning, dimensionality reduction and so on (Buest, 2017). This bottom-line object is technologically achievable but whether the end result is compliant with social, legal, economic and political norms are questions everyone is pondering on. When these questions have propped and gradually when there will be a structure regulating and legitimizing AI ethics as a mandatory norm, there will arise a need for checks and balances. Unfortunately, there is no system to regenerate the

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reasoning behind results manufactured by AI devices. A comprehensive and meaningful result analysis where the origin of bias can be meted out is naturally unavailable. With AI devices making into the mainstream industry, a level of trust will be generated and bias can easily be justified but not be called out. This is an issue because once AI is deployed in culturally sensitive issues and an unfair result is produced, there will be no remedy or redress for the same. Technology (AI) and Cultural Heritage Technology, including Artificial Intelligence through internet/WWW/cloud technology spans State boundaries and provides a platform to interact with domestic as well as international entities or individuals. On the other hand cultural heritage is geography specific, community specific and group specific. Modern cultural heritage might have cross boundaries references but in the parlance of international conventions, most of the cultural heritage subjects are contained within particular groups. Such culture is preserved well, recreated and passed on from generations to generations. Technology has been able to create new verticals and intrude in the normal domain of culture. As mentioned in the abovementioned paragraphs, culture can be a sensitive issue hence technologies where a lot of working happens backstage, can create issues with right to dignity of people. Following is a case study of Yahoo in a global domain.

Conjunction with AI The conjunction of international cultural law with AI (especially AI Ethics as a soft law) is enumerated as follows: 1. AI & Enculturation 2. Cultural Policy & the Self-Reflective Trait of AI Ethics as a Soft Law 3. ‘Identitarianism’ & the Ethics of Explainable Artificial Intelligence 4. Responsible Artificial Intelligence & Ethnocentrism cum Exclusivism 5. Civilized AI & International Law on Cultural Heritage In continuation, we will follow the SOTP classification as we had discussed in the chapter on International Human Rights Law. Since culture and international law are closest to the field of international human rights, this section is also a specific follow-up to the IHRL chapter, with regards to issues related to international cultural law.

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AI & Enculturation Enculturation is a term belonging to sociology, which simply means that a generic and ordinary process where a set of established institutions in a society, generally on a generational basis, pass off and teach their inherited mores, folk, traditions and other components & aspects of their cultures to those who learn with it and through it. The definition is not absolute, but is reflective of the gist of what the term constitutes. Now, disruptive technologies, are the drivers of social control (Marx, 2015; Dupont, 2017; Miracola, 2019; Artificial Intelligence as a Sociological Phenomenon, 1989; Soelistyo, 2021; Anderson, 2020) and they are at least capable to influence the cultural manifestations inherited and practised by people. The scientific corollaries at substantive levels do not change, because scientific objectivity is to be preserved. However, at procedural levels, how the tools are assimilated or used to replace or improve the status quo clearly indicates how the technology renders social control. AI involves in the enculturation of societies, and is definitely involved in the cultural changes and processes in societies around the world. One of the best examples that can be taken is TikTok (Zha, 2021; Alexander, 2020; Wang, 2020), which uses algorithms to provide a behaviour/activity-centric recommendations system, instead of the old traditional like-share-subscribe system of popularity and cyber virality. Using the SOTP Classification, here are the following commonalities we can seek in the primary nexus between AI, Law & Culture: • As a Subject, AI’s technological ability decides how it interprets the content, which represents or manifests cultural heritage. Since cultural content has its own organic human or community-based biases, it is very clear that the way the AI interprets it, processes it and behaves accordingly would show forth how it generally behaves and shows results. It has to be understood that cultural biases or cultural difference (both are not the same) must be treated in a positive manner, and not in an anachronistic manner, to avoid any detriment to cultural and individual rights. As a Subject, how the AI is manifestly available, and what is the general trend of it to learn and process decides how it becomes a subject of enculturation. It may differ or may not differ among various kinds of environments and so on; • As an Object, we already have existent examples (TikTok was one of them). Therefore, the question of explicability will definitely come into being, thereby establishing possible opportunities for how as an Object, the AI is designed to explicate its activities. The legal concept of privacy by design and default directs us towards the same understanding. Taking from the chapter on international human rights law, it would be reasonable to assert that the

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manifest availability of the object and the way it asserts itself to enculturation or renders enculturation would be sufficient limitedly; • As a third party, there is no clear practical example available, but, in a speculative manner, it can be suggested that the anthropomorphic and personified quality of AI as a third party should be analysed reasonably. Once that is confirmed, then maybe more strands of understanding can be developed; The further sub-sections are based on the SOTP and CEI classifications made in the previous chapters of the handbook. Cultural Policy & the Self-Reflective Trait of AI Ethics as a Soft Law Culture represents itself with many interesting paradigms in international law and relations: it can represent sovereign equality and inviolability, then it can also represent how countries treat individual and group rights, and sometimes, cultures are essential to understand technology/economic/political policies are developed. Now, the literature of AI Ethics is usually dominated in the United States, Canada and the Council of Europe’s member-states. For example, China is also contributing into AI Ethics with its own ‘Chinese characteristics’, while the UAE and Saudi Arabia generally focus on more economic upskilling and supplementation of their polities through innovative disruptive technologies. India’s policy on AI is more welfare-centric and MSME-centric, which shows a cautious tendency to accept industry innovation in AI, while the US is clear and very much direct in its security uptakes on AI Research. In order to declutter and estimate how AI Ethics is influenced by cultural policy, here are the following reasons on the self-reflective behaviour of AI Ethics: • AI Ethics is an important and strategic part of technology policies, which reflects the national, foreign and internal priorities of the government’s interests to democratize and utilize the technology for economic, diplomatic and other legitimate causes; • AI Ethics is self-reflective to the cultural nature of societies and civilizations, because they just not only represent the needs and trends of a country and its people, but also reflect how such measures are proportionately applied and put into the best use possible; • AI Ethics resembles the constitutional, civilizational and public value systems as well (both moral and ethical). It is by the virtue of technology ethics that we can estimate how cultural relations and culture-inspired strategic intrusions can be made, because AI bridges the connectivity between physical spaces, cyber/digital spaces & the space of information & perception warfare;

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• In terms of implementing, respecting and expanding the juridical scope and relevance of international technology law, AI Ethics indeed has an important role, as it defines the emerging trajectories in technology ethics and jurisprudence. States can opt for monistic or dualistic approaches in wherever possible steps they undertake, which also is contributory to the development of multilateralism; ‘Identitarianism’ & the Ethics of Explainable Artificial Intelligence Identity is an important aspect of the political relevance of technology in the realpolitik. Identities can be of many kinds, and their structural formations also do differ, but AI explanability would be essentially important in the context of international cultural law. • Explanability offers a serious opportunity to understand how indifferently/differently human actors and the manifestly available AI understand and process points of identity. This stems beyond the question of privacy of design & default, because here, a real estimate of AI’s explanability can probably explain how the manifest availability and utilization of AI offers possible considerations over ensuring that cultural and individual liberties are protected; • Towards the angle of policy, identities can be used dynamically. Like TikTok, applications, for example can be used to target practices or activities, which may amount to ethnocentrism or cultural appropriation. If effective policies are developed on the relevance and transformation of XAI policies in the perspective of identity-based or identity-centric algorithmic activities and operations, then the technocratic democratization of cyberspace can be tackled in a reasonable manner; Responsible Artificial Intelligence & Ethnocentrism cum Exclusivism • Ethnocentrism and exclusivism generally relate to cultural practices which are limited to certain places, but the biases or traditional viewpoints asserted by the populace of those places are generalized and imposed on other communities and places as well. It happens in the context of countries like the United States, the UK and even China; • If a responsible AI policy has to be developed which counters ethnocentrism on the otherness problem on AI Ethics and technology-related thought leadership, it is possible to resolve & find relevant solutions. A transient and multimodal way of analyzing the value systems across different societies, respecting and preserving their indigenous rights and dignity should be the

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best way. The bargain for protecting individual rights and cultural rights at the same equilaterals must be retained by countries as civilizational states by applying the principle of sovereignty over cultural heritage and presenting it as some sort of sovereign equality among countries so that they are wellrepresented; • Like the United Nations Alliance on Civilizations, policies should be made to encourage intercultural confidence-building measures. Generalized and narrowly-substantiated means of development of Responsible AI policies, which are most of the times West-centric should be avoided. Some Westlessness would surely not be a big issue if democratic countries are reasonably given opportunities. The role of non-state actors would be scrutinized, but anyways would be important; Civilized AI & International Law on Cultural Heritage In international law on cultural heritage, considering the CEI classification, artificial intelligence must be regarded on all the three aspects rigorously: as an Entity, as a Concept and also as an Industry: • The Concept basis for AI is simple and should be preserved under international cultural law reasonably. Just because there should be a global basis to determine AI Ethics, it does not mean we would ignore legal pluralism over conceptualizing AI. Except in compliance matters in international law, the scholarship over AI Ethics and International Law is reflective of the sovereign considerations of countries, and shows encultured inclinations as well for the concept and its fertilization; • As an Entity, Legal/Juristic, leaving the possibility of double SOTP-based scrutiny26, the cultural implications of the manifest availability of AI and its machinic empathy would be important to cover any sub-issues in international law and policy, where there exist any intersectional disputes over AI Policy; • As an industry, there should be a clear balance in endorsing cultural diplomacy and generalist morality. The role of bodies such as UNAOC and UNESCO should be to ensure achieving better standards, rule of law and compliance. Ideological or political obscuration of identity issues, in the name of identity politics, should always be avoided. Ethical considerations must triumph moral considerations in industry-related compliance matters;

Please refer to the chapter on international human rights law.

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Case Studies Yahoo!, Inc. v. La Ligue Contre Le Racisme et L’Antisemitisme (N.D California, 2001) • Yahoo!, Inc. (hereinafter referred to as “Yahoo”) is a corporation incorporated as per the laws of Delaware having its principal place of business in Santa Clara, California. The company is primarily involved in providing internet and other services which can be accessed through a Uniform Resource Locator (URL) i.e http://www.yahoo.com. There are subsidiary corporations of Yahoo which operate through regional offices in different countries. These regional offices operate through different domains like Yahoo.in for India, Yahoo.fr for France and so on. • La Ligue Contre Le Racisme et L’Antisemitisme (hereinafter referred to as “LICRA”) and L’Union Des Etudiants Juifs De France, citizens of France are non-governmental organizations based in France. The primary object of these organizations is to work for eradication of anti-Semitism. • Since its inception, Yahoo provided services through which people can have an enhanced experience of communication and interaction. Through its encompassing platform Yahoo provides email services, shopping services, chat rooms, web hosting pages and so on. Since it was a platform being used by many, it provided services for buying and selling online wherein goods like drugs and other illegal substances came to be sold as well. Yahoo maintained that it only acts as a facilitator and has no role to play in the private transaction between the buyer and seller. • Yahoo’s auction site provides for people to post ads about goods they want to sell which can be accessed and seen by any global user through a computer. In Yahoo France several goods pertaining to Nazi propaganda and Third Reich Memorabilia, evidences against Holocaust came to be shown for sale. This was openly being accessed by the globe. • It is not only a ethical crime but also a legal prohibition on sale of such goods. This legal system in France is created due to the local culture/community. On account of pro-Nazi goods being available on Yahoo France’s extranet, LICRA came to issue a “cease and desist” order to Yahoo France and ultimately filed a Criminal complaint before Tribunal De Grande Instance De Paris (hereinafter referred to as “the French Court”). • After hearing both the parties the French Court identified that there are more than 1000 pro-Nazi goods. The Court observed that such goods are available to general public either on Yahoo.com or through a link on Yahoo.fr. On account of the same the French Court ordered the following:

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─ eliminate French citizens’ access to any material on the Yahoo.com auction site that offers for sale any Nazi objects, relics, insignia, emblems, and flags; ─ eliminate French citizens’ access to web pages on Yahoo.com displaying text, extracts, or quotations from Mein Kampf and Protocol of the Elders of Zion; ─ post a warning to French citizens on Yahoo.fr that any search through Yahoo. commay lead to sites containing material prohibited by Section R645–1 of the French Criminal Code, and that such viewing of the prohibited material may result in legal action against the Internet user; ─ remove from all browser directories accessible in the French Republic index headings entitled “negationists” and from all hypertext links the equation of “negationists” under the heading “Holocaust.” The order subjects Yahoo! to a penalty of 100,000 Euros for each day that it fails to comply with the order. Yahoo requested the French Court to reconsider as it could very well post a warning on Yahoo France but it was technologically impossible for it to ban access of French citizens to Nazi goods available on other domains or Yahoo.com. It would be against its rights of First Amendment in the United States if it imposes a blanket ban on Nazi goods throughout the world. Apart from that, if such a precedent is set then every good at some point would violate a particular legal or cultural order in a restricted geography and because of that taking it out from the total ecosystem is commercially impossible. Yahoo attempted to overturn the impact of the French Court order by filing a case in the United States seeking enforcement of its fundamental rights. Hence, the case before the U.S Courts posed a novel challenge and a global issue. The U.S Court noted that this case is not about whether Nazis were responsible for the Holocaust and whether it is morally right to ban such goods. Everyone knows that Nazis were responsible and it is utterly offensive to use such goods. It is also not the case to determine the authority of French Court to implement such a ban. Obviously, France had faced the inhumane actions by Nazis so they can preserve all the right to condemn it. This case is purely to determine the right of the corporation as per the United States Constitution. Ultimately, the motion of the Corporation seeking declaration in its favour was granted.

Here lies the cultural dilemma in international sphere and the wrath of technology. It did not even require a complex technology to have intruded in

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one’s cultural space. In France, an issue which is highly sensitive, comtemtable and punishable is protected under the First Amendment of the U.S Constitution. Yahoo had specifically quoted that it is “technologically impossible” for them to impose a complete ban on such goods. Artificial Intelligence with its capabilities could bring similar unprecedented harms to cultural diversity and heritage (Renteln, 2010). Integrations between AI & Culture In a seminar titled E-relevance of Culture in the Age of AI (Council of Europe, 2018) conducted in Croatia in 2018, importance of retaining culture and creativity in the age of ultra-automation was discussed in depth. Some of the questions revolved around ways and means to keep production of culture relevant in this day and age of automation, how would AI impact the human uniqueness when the bottom line of AI is replicating and producing after minor tweaks? Would it only facilitate better innovation and creativity? To start with it, culture and creativity should be one of the parameters while discussing information technology. Culture, today is not even part of conversation when discussing science or a new innovation. Development of a new sustainable model should include and have a cultural agenda. AI ethics norms should have a cultural agenda. A greater possibility of discrimination would arise due to the unequal documenting of cultural heritage. In one part of the world, traditions must have been archived in a structured manner while some place else, this would not be the case. In such a situation, the data fed into AI device would include only the structured archived traditions and lose out on lesser-known but highly important cultural heritage. This would automatically lead to a biased result highlighting only one point of view. Protection & Proliferation of Culture by not being WEIRD WEIRD really means: W – Western E – Educated I – Industrialized R – Rich D – Democratic Most people are not WEIRD (Joseph Henrich, 2010). It could be a possible cause of inherent bias. What happens is, due to most of the literature written is relevant to the western world, over-generalization takes place which hampers

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the issues of people in other parts of the world. Applying this to the context of Culture is very important. It is local heritage culture of the non-West which is immensely resourceful but has not received the importance it should receive. It has much to offer and for the world, it is a hidden treasure. AI is the future for several strides of businesses, governments and other functionaries. It is predicted that the brain of AI will be globally connected and all the information in the world at its disposal. To ensure a culturally-conscious AI system, one needs to ensure that Culture is holistically onboarded to an AI system.

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International Health Law

Dev Tejnani

Introduction With the advancements in the field of Artificial Intelligence, there have been innumerable developments in the field of AI and how it impacts the International Healthcare laws. It goes without saying that since AI and Machine Learning (ML) has reached its zenith, there have been a number of developments in the field of healthcare and these developments have made the lives of doctors and medical specialists easier, since these machines that are used when it comes to treating individuals have become much more efficient and much more robust. However, with the developments, there are also a host of ethical and legal challenges that come along with these developments. For instance, it is imperative to firstly understand what AI is and then focus upon the legal and the ethical challenges that various countries have faced or are facing when it comes to addressing or rather understanding the laws pertaining to healthcare and the usage of AI in the field of healthcare. It is imperative to understand the trends and the strategies that are adhered to by other countries, for instance, the approach and the strategy taken by the United States or Europe, which will inherently lead to the aspects pertaining to the ethical and legal challenges with regards to AI-driven healthcare. Furthermore, it is extremely necessary to focus upon the various ethical issues that follow when it comes to the usage of AI-driven healthcare instruments. For instance, the consent, how safe and transparent is the process when it comes to the usage of AI in healthcare, the various algorithmic biases and trials which may follow and lastly whether the machine has the capacity to maintain data privacy of its patients just like how a doctor maintains a doctor-patient privilege. Apart from focusing upon the ethical issues, it is also imperative to shed light upon the legal issues, for instance, whether the AI driven data is protected under the intellectual property laws of the country in which it is used, whether the various measures in order to protect the cybersecurity of these machines are in place and lastly whether the algorithms and the data that is used is in consonance to the data protection or data privacy laws of the country in which such a system is adopted.

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Legal Background AI has the capacity and the capability to revolutionise healthcare. It is extremely imperative to understand that right from addressing the various basic issues such as imaging and diagnostics to the proper allocation of resources in a hospital, healthcare applications and algorithms which are designed specifically to understand these aspects can play a major role and can be deemed to be regarded as a game changer in the field of AI and healthcare. A number of economists are of the opinion that AI has the capacity to bring about a paradigm shift in the way the health market operates and in the years that follow, the health market can approximately multiply into 10 times of what it is at present (Accenture, 2017) . However, regardless of how crucial these developments are, it is imperative to understand that AI powered applications which may be incorporated to assist the medical practitioners need to be incorporated in a systematic manner, i.e. they need to be incorporated after taking into consideration the host of ethical and legal issues that follow from it and the law making authorities inter alia do need to consider the ethical and legal challenges which the implementation of AI will bring about in the healthcare sector. It can be deemed to be regarded that AI in the health care sector has an untapped potential and in order to tap this potential that AI possesses, it is highly imperative to incorporate the various stakeholders such as AI system creators, doctors, medical professionals, patients, ethicists and the law making authorities, since these stakeholders will be majorly affected with the introduction of AI in the field of Healthcare and how the laws that revolve around it will determine whether AI can successfully be implemented in the healthcare sector. What is Artificial Intelligence? Artificial Intelligence can be deemed to be regarded as a network which comprises of a number of artificial neural networks which incorporate a number of data sets and comprise of a number of layers which have the capability to understand and analyse patterns which are programmed in a huge data set (Artificial Intelligence in Healthcare, 2018). However, there is no precise definition of Artificial Intelligence, or, “AI” which is its abbreviation, but it is imperative to look upon the various subtypes of AI. Machine Learning (ML), can be deemed to be regarded as a subset of AI, and this has been one of the most prominent approach when it comes to dealing with the various aspects revolving around AI and healthcare, since machine learning has the capacity and the capability to understand and analyse the various systems which deal with the computation of the medical data of various individuals and these

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machines have the capacity to analyse and monitor the performance of this data without explicitly hampering its progress (Artificial Intelligence in Healthcare, 2018). Lastly, it is imperative to understand the legal and the ethical implications that surround AI and Machine Learning (ML) and how these ML algorithms can be deemed to be regarded in consonance to “black boxes” or “black box AI”, which means the outcomes can be extremely difficult to decipher when the outcome is provided by the machine to the medical professional. Basically, the interpretation could be confusing for the clinicians or the doctors to fathom or interpret completely (Machine Learning, natural language programming, and electronic health records: The Next Step in the artificial intelligence journey?, 2018). Trends and Strategies The introduction of AI in the healthcare sector brings about a multitude of legal and ethical issues along with it. However, countries like the United States of America and Europe are bolstering their resources in order to compete with each other against the largest Asian competition that they face in the form of China and are constantly striving to develop their laws and their ethical principles when it comes to making use of AI products in the field of healthcare.

Conjunction with Artificial Intelligence Important Ethical Challenges The importance of using AI and ML techniques in clinical practice can be deemed to be regarded as the next big thing and it can be said to have immense potential in the long run, however, along with the benefits, there also arise a host of ethical issues which need to be adhered to. Informed Consent while usage. AI based applications which focus on healthcare, such as imaging software’s, diagnostic machines, surgical instruments which are autonomous in nature, have the capacity to change the way the relation that a patient has with his/her clinician. However, it is imperative to understand that the usage of AI based applications can deem to render the principles of informed consent of the patients and this can be deemed to be regarded as an extremely pressing issue, which has not been given the importance that it should have received. In fact, integrating AI based applications in the field of healthcare would in fact bring along a series of challenges (Cohen, et al., 2014). Before AI based applications can be completely adopted and used, it is imperative to understand that there

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are certain principles which need to be specifically focused upon and these principles can be deemed to be regarded as the principles of informed consent, which need to be deployed whilst applying AI in the healthcare sector. In fact, another important aspect that arises here is whether it is the implied duty of the clinician to inform the patients beforehand that they would be using an AI machine and it should be made compulsory for clinicians to explain the various vulnerabilities and the various complexities that revolve around the usage of AI, including the usage of ML that is used by the system, the kind of data that it runs and at the same time whether it has the possibility of running biases or other shortcomings that arise while a particular data set is being accessed and under what circumstances should the clinician approach and inform the patient that AI is being relied upon. It is extremely crucial to understand that all these issues cannot be addressed easily, especially if the AI system’s algorithm runs on “black-box algorithm”, which may deem to regard certain non-interpretable machine learning techniques to be deemed to be regarded as extremely difficult for the clinicians to perfectly or fully understand (Harvard Law Today, 2018). As discussed above, Corti as the capacity to analyse and determine whether a patient is suffering from an out of hospital cardiac problem and whether he/she may get a cardiac arrest and this software can make emergency calls at lightening speeds and in an extremely accurate manner, much faster than humans can and ensure that the requisite symptoms are analysed in a timebound manner, i.e. the tone of the voice of the patient, the breathing patterns of the patient and all other requisite or necessary data (Vincent, 2018), however, the algorithm which Corti runs on can be deemed to be regarded as, “blackbox” algorithm since the investors who invested in the development of Corti were not aware with regards to whether the software had the capacity to make decisions in a time-bound manner, in order to make sure that the patient is aware that an individual is suffering from a cardiac arrest, this can in fact be deemed to be regarded as extremely dangerous since lack of knowledge is always an issue, especially a bigger issue in the medical field. In fact, AI Chatbots and health applications which apply ML techniques are widely being used by a number of individuals and these applications mainly consist of applications that vary from providing guidance to an individual when it comes to monitoring his/her health conditions or when it comes to improving the level of medication that an individual need to take and at the same time it also has the capacity to analyse the data which is collected by a wearable or by a smart watch.

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Case Studies Approach taken by the United States Back when Mr. Barrack Obama was the President of the United States of America, the reports which were drafted and published by the US governmental agencies, clearly showed that AI needs to be given due attention inter alia, the report specifically elucidated upon how necessary it is for the United States to compete at the global level when it comes to incorporating AI tools and how AI can be used in order to bring about fairness, safety and governance in the healthcare sector (US Government, 2016). There were a number of reports made by the United States Government during Mr. Barrack Obama’s tenure which specifically enumerated upon how AI can be used in order to bring about fairness, transparency and accountability-by-design if an ethical ecosystem is built around the use of AI (US Government, 2016). However, ever since Mr. Donald Trump took over as the President of the United States, the AI adoption method of the United States was deemed to be regarded as a method which was more inclined towards a free-market approach (Dutton, 2018). In fact, it is imperative to note that the White House decided to carry out an AI American Industry Summit in the year 2018, which was organized in the month of May, 2018 and the Trump Administration volunteered the organization of this summit which provided an insight into how the various regulatory barriers to AI and innovation could be avoided and how AI could be the next big thing (White House, 2018). In fact, it is extremely imperative to note that in the year 2018, the Executive Office of the President of the United States opined that it aims to allot the highest Research and Development budget in order to bolster its AI resources by the year 2020 (White House, 2018). However, in the year 2019, President Trump signed an order which specifically enumerated upon the Executive Order with regards to Maintaining and Carrying out an American Leadership Programme in the field of Artificial Intelligence. This Executive Order was signed by the President in order to deal with the backlash that the United States had faced when it took a hands-off approach with regards to the usage of AI in comparison to its fierce Asian rival, the China (Knight, 2019). In lieu of this Executive Order, President Trump decided to adopt a strategy which would enable him to lead the United States in the right direction, making it the most prominent country in the field of AI. This Executive Order which was passed mainly focused on bolstering the resources of the Federal Government and it mainly consisted of the following five agendas: 1) Investment in the field of AI Research and Development (AI R&D); 2) understanding the potential of AI resources; 3) maintaining a global standard when it comes to marking a niche in the field of AI; 4) creation of a taskforce or workforce which would specifically focus on

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bolstering its resources towards the development of AI; and 5) engaging with its other subordinate countries and allies in order to protect its AI resources on a global front (White House, 2018). In fact, the United States White House has been working towards the creation of a website called, “AI.gov”, which can be deemed to be regarded as a website which specifically focuses on the various opportunities that an individual in the United States can make use of in the field of AI and it also provides an opportunity to individuals who are eager and want to explore more in the field of AI. With regards to AI and Healthcare, the United States Government has passed innumerable bills with special emphasis on AI and these bills have been introduced in the Congress ever since Donald Trump was sworn in as the President of the United States of America. A number of bills such as the, “SELF DRIVE Act (H.R. 3388)”, “FUTURE of Artificial Intelligence Act of 2017, (H.R. 4625 and S.2217), and the, “AI JOBS Act of 2019 (H.R. 827) were introduced in the US Congress as a means to promote the usage and the importance of AI. However, the “SELF DRIVE” Act is the only bill which was passed by the House of Representatives. It was held that these bills were specifically made in order to promote AI in Healthcare, however, all these bills did not quite focus on the ethical and the legal issues surrounding the usage of AI tools in the field of healthcare. Furthermore, the bill of the “FUTURE of Artificial Intelligence Act of 2017”, for instance, had a clause which specifically enumerated that the Secretary of Commerce was deemed to be regarded as the head when it came to setting up a Federal Advisory committee which had the powers to give its opinion and provide its advice to the Secretary of the State27. The Committee was also conferred various powers under the provisions of the aforementioned Act which would enable the committee to take decisions with regards to incorporating and determining the various ethical standards that it needs to incorporate in order to ensure that AI tools are implemented in a swift and smooth manner. Basically, the committee was conferred powers in order to ensure if AI could easily be made a part of the healthcare sector in an efficient and a cost-effective manner28. In fact, it is quite interesting to note that there have been ample of legal developments that have taken place in the United States with regards to the usage of AI in the field of healthcare; in the State of California, the legislative bodies went ahead and sanctioned unanimously a bill in the year 2018, which specifically focused on 23 Asilomar AI principles (Future of Life, 2018). AI tools are already significantly used in a number of clinics in the United States. For instance, AI can be deemed to be regarded as having immense potential in the field of carrying diagnosis and imaging. Furthermore, the Food 27 28

Section 4(a) and Section 4(b)(1) of the FUTURE of Artificial Intelligence Act of 2017. Section 4(b)(2)(L) of the FUTURE of Artificial Intelligence Act of 2017.

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and Drug Administration Department (FDA) has already given a nod or has already approved the sanction of approximately 40 AI-operated medical devices which are actively being used by hospitals and clinics in the United States (E.J., 2019). In fact, it is imperative to cite the example of “Arterys”. The “Arterys” was granted a sanction by the United States Department of Food and Drug Administration (US FDA) in order for it to use its medical imaging platform as the first application which worked on algorithms and applied machine learning techniques in order to carry out medical imaging in clinical practice (Marr, 2017). It is imperative to note that “Arterys” was initially given a nod for its usage in the cardiac-magnetic resonance image analysis, however, Arterys was later also given a sanction by the FDA for its usage in comparison to other substantially equivalent devices29. Another important example which shows that the United States has made significant development when it comes to incorporating AI tools in the field of healthcare is the development of, “IDx-DR”. “IDx-DR” can be deemed to be regarded as the first ever AI-powered diagnostic machine which was granted a sanction by the FDA. This machine has the capacity to analyse and provide an automated result without any human intervention whatsoever. This machine uses algorithms in order to analyse and come up with an autonomous screening decision which does not require any human to be present or analyse the results (U.S. Food & Drug Administration, 2018). In this machine, the only job that requires a human to be present is when the physician has to furnish the details of the patient, i.e. the physician needs to upload the patient’s retina scans to a cloud server and then the IDx-DR software has the capacity to guide the physician what further steps the patient needs to take. For instance, if the patient should do a re-scan within a period of 12 months, or whether the patient should visit an eye-specialist in order to check whether there is any symptom of diabetic retinopathy (U.S. Food & Drug Administration, 2018). Furthermore, in the year 2018, the FDA went ahead and granted further sanctions to Imagen’s AI powered software called the, “OsteoDetect” which enabled the physicians to analyse and fathom common or simple types of wrist fractures, which could be deemed to be regarded as a distal radius fracture and this can be deemed to be regarded as a common type of fracture amongst adult patients (U.S. Food & Drug Administration, 2018). Basically, OsteoDetect made use of Machine Learning techniques or algorithms in order to fathom two-dimensional X-Ray image which could be used by the physicians and the doctors to understand and explain the patient the type of fracture he/she was suffering from.

Arterys. https://www.arterys.com; 2019.

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Approach taken by the Europe The European Commission decided to adapt a comprehensive strategy with regards to the development of AI in the field of healthcare in the year 2018. With regards to this strategy, the Commission (European Commission, 2018), decided to come up with an initiative which specifically dealt with AI and the initiative inter alia aimed to provide a robust framework with regards to protecting the ethical and legal issues, for instance, the development of an AI Alliance or developing an AI ethics guideline which countries in the EU could adhere to. The Commission also stressed upon the importance of creating a proper legal regime which could cover under its ambit all the possible laws and all the possible regulations which would aid towards establishing a robust healthcare regime in the European Union (EU) and this could be used in order to make sure that the amount of public and private investment in AI can at least be reached to 20 Billion Euros by the end of 2020. The European Commission’s High Level Expert Group which deals with the aspects of AI was appointed in the year 2018 by the European High Commission and this group can be deemed to be regarded as a group which is working towards the creation and establishment of a European AI Alliance, the details of which were published in the Ethical Guidelines in the month of April, 2019. The Guidelines are made on the lines of promoting the use of “Trustworthy AI” and it takes under its ambit the following seven aspects which are extremely crucial for an AI system to adhere to, 1) the AI software should be a part of a human agency and there should be constant human oversight over the development of the AI software; 2) the AI software should be built in a way which makes it compliant to all sorts of attacks and it should be deemed to be regarded as a software which cannot be rendered vulnerable easily; 3) the AI software should take into account the privacy and the various aspects pertaining to data governance; 4) it should be transparent and should provide all the necessary data that may be required; 5) the AI tool should not discriminate and maintain fairness at all times; 6) the AI tool should maintain and take into account the various aspects revolving around environmental and societal well-being; and 7) it should specifically focus on accountability (European Commission, 2019). In order to furnish its requisite deliverables, the AI HLEG also went ahead and decided to publish a document with special emphasis on how crucial and important AI is. In fact, in the year 2019, the AI HLEG also went ahead and published another document which specifically emphasised on “Policy and Investment Recommendations for promoting Trustworthy AI” (European Union, 2019). The EU Commission further elucidated upon how imperative it is for all the members in the EU to develop and follow a comprehensive plan with regards to the usage of AI, adapting and adhering to a national AI strategy. It is imperative to throw light upon the fact

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that the United Kingdom’s Department of Health and Social Care is already bolstering its services in this direction, i.e., of adapting a comprehensive method in order to enable the usage of AI and ML tools in the healthcare sector (United Kingdom Government, 2019). The Commission is striving to work towards motivating the EU Member States such as, Norway, and Switzerland to adapt a coordinated plan which takes into consideration the usage of AI and associated technologies in the European Union. The overall working of this Commission can deem to regard Europe as one of the world’s largest region when it comes to making a cutting-edge, ethical and secure AI software (European Commission, 2018). There are already a number of AI health related applications which are used by a number of hospitals and a number of organizations in the European Union and as a matter of fact, there are many other such health applications which companies are working on. For instance, Ada30 can be deemed to be regarded as an AI based healthcare application which basically has the capacity to understand and analyse a symptom or a series of symptoms that an individual may suffer from and then it may analyse and provide the patient with the necessary guidance, for instance, explaining the user how imperative it is to visit a doctor or seek medical emergency. Ada can be deemed to be regarded as a software which has been certified with a CE Mark (Class-I), which is the first and the foremost aspect that needs to be adhered to when it comes to putting a medical software in the market in Europe. The Ada machine also can be deemed to be regarded as compliant with the EU General Data Protection Regulation, 2016/679 (GDPR). Another example which is worth citing here is the research undertaken by the doctors and the researchers at the DeepMind and Moorfields Hospital in London, UK are striving to work towards making a software which can be deemed to be regarded as an AI system which has the capacity to analyse eye scans and make referral recommendation, consisting of approximately 50 common diagnoses. The system has been tested on approximately 14,884 scans and the system apparently showed a success rate of 94% (De Fauw, et al., 2018). The algorithm used by DeepMind’s health team can be deemed to be regarded as an algorithm which is transitioned to Google Health, and the Moorfields Eye Hospital is, “extremely elated to work and partner with Google Health on the next phase which aims to further develop and make an AI system which is extremely compliant and addresses the issues of patients across the globe (Moorfields Eye Hospital, 2019).” In fact, Ultromics31, is yet another example wherein a team of experts at the University of Oxford bolstered their resources in order to creating a programme which had the capacity to reduce misdiagnosis 30 31

Ada. Your Personal Health Guide, https://ada.com; 2020. Ultromics. https://www.ultromics.com; 2019.

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and at the same had the capacity to identify the early signs of cardiovascular diseases. Ultromics’s EchoGo Pro, for instance, can be deemed to be regarded as an AI-based tool which has been conferred with a CE marking in Europe that had the capacity to analyse coronary artery disease at ground root levels. Corti32 is another example wherein a software was designed by a Danish company which had the capabilities to use ML techniques in order to analyse and take emergency dispatch decisions. Corti has the capacity to analyse and determine whether a patient is suffering from an out of hospital cardiac problem and whether he/she may get a cardiac arrest and this software can make emergency calls at lightening speeds and in an extremely accurate manner, much faster than humans can and ensure that the requisite symptoms are analysed in a time-bound manner, i.e. the tone of the voice of the patient, the breathing patterns of the patient and all other requisite or necessary data (Vincent, 2018).

Corti A. Co-Pilot for medical interviews, https://corti.ai;

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List of References

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Russell, Stuart J and Norvig, Peter. 2015. Artificial Intelligence: A Modern Approach. 2015. Sutton, Richard S and Barto, Andrew G. 2018. Reinforcement Learning: An Introduction. 2018. The Andes Physics Tutoring System: Lessons Learned. Vanlehn, K., et al. 2005. 3, 2005, International Journal of Artificial Intelligence in Education, Vol. 15, pp. 147-204. The Defining Characteristics of Intelligent Tutoring Systems Re-search: ITSs Care, Precisely. Self, J. 1999. 1999, International Journal of Artificial Intelligence in Education (IJAIEd), Vol. 10, pp. 350-364. The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. VanLehn, K. 2011. 4, 2011, Educational Psychologist, Vol. 46, pp. 197-221. Trilling, B. and Fadel, C. 2009. 21st Century Skills: learning for life in our times. s.l. : John Wiley & Sons, 2009. Wachter, Sandra, Mittelstadt, Brent and Russell, Chris. 2020. Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI. SSRN. [Online] March 3, 2020. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3547922.

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A Misdirected Principle with a Catch: Explicability for AI. Robbins, Scott. 2019. s.l. : Springer, 2019, Minds and Machines, Vol. 29. Arbitral.com. Omnipotence test for AI safety. Arbitral. [Online] https://arbital.com/p/omni_test/. Bjola, Corneliu. 2020. EDA Working Paper - Artificial Intelligence. Emirates Diplomatic Academy. [Online] January 2020. https://static1.squarespace.com/static/52c8df77e4b0d4d2bd039977/t/5e46b92b53c6246 6ee47319c/1581693242795/EDA+Working+Paper_Artificial+Intelligence_EN+copy.pd f. Council of Europe. 2018. Responsibility and AI. Council of Europe. [Online] 2018. https://rm.coe.int/responsability-and-ai-en/168097d9c5. European Parliament. 2020. Artificial Intelligence and Civil Liability (PE 621.926. 9). European Parliament. [Online] 2020. https://www.europarl.europa.eu/RegData/etudes/STUD/2020/621926/IPOL_STU(20 20)621926_EN.pdf. —. 2017. European Parliament Resolution of 16 February 2017 with Recommendations to the Commission on Civil Law Rules on Robotics (2015/2103(INL), doc. no. P8_TA(2017)0051. European Parliament. [Online] February 16, 2017. http://www.europarl.europa.eu/sides/getDoc.do?type=TA&reference=P8-TA-20170051&language=EN&ring=A8-2017-0005. Harvard Law Petrie-Flom Center. 2017. AI Citizen Sophia and Legal Status. [Online] November 9, 2017. https://blog.petrieflom.law.harvard.edu/2017/11/09/ai-citizensophia-and-legal-status/. House of Commons Science and Technology Committee. 2018. Algorithms in Decision-Making (HC 351). [Online] May 2018. https://publications.parliament.uk/pa/cm201719/cmselect/cmsctech/351/35104.htm#_ idTextAnchor010. More, Hemant. 2020. Schools of International Law. The Fact Finder. [Online] November 09, 2020. [Cited: February 01, 2021.]

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International Law, Diplomacy and Dispute Settlement • • • • • • • • •

"Quasi Track-One" Diplomacy: An Analysis of the Geneva Process in the Israeli-Palestinian Conflict. Schiff, A. 2010. 2, 2010, International Studies Perspectives, Vol. 11, pp. 93-111. Asthana, Subodh. 2019. Functions, Privileges and Immunities of a Diplomat. ipleaders. [Online] June 19, 2019. [Cited: December 16, 2020.] https://blog.ipleaders.in/all-aboutdiplomat/. Bernard Marr. 28 Best Quotes About Artificial Intelligence. Bernard Marr. [Online] [Cited: February 03, 2021.] https://www.bernardmarr.com/default.asp?contentID=1158. Bjola, Dr. Corneliu. 2020. Diplomacy in the Age of Artificial Intelligence. Diplomacy in the Age of Artificial Intelligence EDA Working Paper. Abu Dhabi, United Arab Emirates : Emirates Diplomatic Academy, January 2020. Chowdhury, Naureen Fink. 2020. Diplomacy at the UN in the Time of Coronavirus. IPI GLOBAL OBSERVATORY. [Online] April 6, 2020. [Cited: December 18, 2020.] https://theglobalobservatory.org/2020/04/diplomacy-at-un-in-time-of-coronavirus/. Congressional Research Service. 2020. Artificial Intelligence and National Security. 2020. R45178. Contemporary Conflict Resolution: the prevention, management and transformation of deadly conflicts. Ramsbotham, O., Woodhouse, T. & Miall, H. 2005. Cambridge & Malden : MA: Polity Press, 2005, Vol. 2. Council of Europe Portal. 1953. Conventions. Treaty Office. [Online] Council of Europe Portal, September 03, 1953. [Cited: December 11, 2020.] https://www.coe.int/en/web/conventions/full-list/-/conventions/treaty/005 . European Court of Human Rights. 2010. European Convention on Human Rights. [Online] 2010. [Cited: December 11, 2020.] https://www.echr.coe.int/Documents/Convention_ENG.pdf.

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Grech, Olesya M. 2006. Virtual Diplomacy - Diplomacy of the Digital Age. Contemporary Diplomacy, University of Malta. s.l. : Faculty of Arts, University of Malta, 2006. Dissertation . House of Commons. 2015. Defence - Seventh Report The situation in Iraq and Syria and the response to Islamiya fi al-Iraq al-Sham(DAESH). 2015. International Committee of the Red Cross. Treaties, States Parties and Commentaries. International Committee of the Red Cross. [Online] [Cited: December 11, 2020.] https://ihldatabases.icrc.org/applic/ihl/ihl.nsf/vwTreaties1949.xsp. INTERNATIONAL CRIMES: JUS COGENS AND OBLIGATIO ERGA OMNES. Bassiouni, M. Cherif. 1996. 4, 1996, LAW AND CONTEMPORARY PROBLEMS, Vol. 59, p. 63. International Dispute Settlement: A Network of Cooperational Duties. Peters, Anne. 2003. 01, 2003, European Journal of International Law, Vol. 14, pp. 1-34. legislation.gov.uk. Consular Relations Act 1968. legislation.gov.uk. [Online] [Cited: December 11, 2020.] https://www.legislation.gov.uk/ukpga/1968/18 . —. 1987. Diplomatic and Consular Premises Act 1987. legislation.gov.uk. [Online] 1987. [Cited: December 11, 2020.] https://www.legislation.gov.uk/ukpga/1987/46. —. 1964. Diplomatic Prileges Act 1964. UK Public General Acts. [Online] 1964. [Cited: December 11, 2020.] https://www.legislation.gov.uk/ukpga/1964/81/contents. —. 1994. Intelligence Services Act 1994. UK Public General Acts. [Online] 1994. [Cited: December 11, 2020.] https://www.legislation.gov.uk/ukpga/1994/13/contents . —. 1998. UK Public General Acts. legislation.gov.uk. [Online] 1998. [Cited: December 11, 2020.] https://www.legislation.gov.uk/ukpga/1998/42/schedule/1. —. 1978. UK Public General Acts. legislation.gov.uk. [Online] 1978. [Cited: December 11, 2020.] https://www.legislation.gov.uk/ukpga/1978/33. Office of Legal Affairs - Codification Division. 1992. Handbook on the Peaceful Settlement of Disputes between States. New York : United Nations Publication, 1992. Organization of American States. Department of International Law (DIL). OAS. [Online] [Cited: December 11, 2020.] http://www.oas.org/en/sla/dil/inter_american_treaties_A-46_diplomatic_asylum.asp. Oxford Learner's Dictionary. Definition of diplomacy noun from the Oxford Advanced Learner's Dictionary. Oxford Learner's Dictionary. [Online] [Cited: September 22, 2020.] https://www.oxfordlearnersdictionaries.com/definition/english/diplomacy. Parliamentary Diplomacy. Weiglas, F & de Boer, G. 2007. 2007, The Hague Journal of Diplomacy, 2:1, pp. 93-99. Proceedings of the American Society of International Law at its Annual Meeting. Grieve, Wilhelm G. 1960. s.l. : Cambridge University Press, 1960. Royal Courts of Justice Strand, London, WC2A 2LL. 2014. Abdul - Hakim Belhaj & Fatima Boudchar V. The Rt. Hon. Jack Straw MP, Sir Mark Allen CMG, The Secret Intelligence Service, The Security Service, The Attorney General, The Foreign & Commonwealth Office, The Home Office & Ors. A2/2014/0596, Royal Courts of Justice Strand, London, WC2A 2LL : s.n., October 30, 2014. Schweizerische Eidgenossenschat Confedaration suisse Confedarazione Swizzera Confederaziun svizra. 2008. ABC of Diplomacy . Bern : Swiss Federal Department of Foreign Affairs (FDFA), 2008. Smith, Gordon S. 1999. Reinventing Diplomacy: A Virtual Necessity. United States Institute of Peace. 1999.

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Aerovel. 2017. Defiant Labs Launches Next Gen Drone: DX-3. Flexrotor Specification. [Online] Defiant Lab Press Release, February 10, 2017. [Cited: January 30, 2021.] http://news.usaonline.us/press-releases/Defiant-Labs-Launches-Next-Gen-Drone-DX3-86383.

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African Union. 2019. African Digital Transformation Strategy and African Union Communication and Advocacy Strategy among major AU initiatives in final declaration of STCCICT3. [Online] October 26, 2019. [Cited: January 14, 2021.] https://au.int/en/pressreleases/20191026/african-digital-transformation-strategy-andafrican-union-communication-and.

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Airforce Technology. Harop Loitering Munitions UCAV System. Airforce Technology. [Online] www.airforce-technology.com/projects/haroploiteringmuniti/.

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Bauer, Riley, Nollet, Shannon and Biaz, Saad. 2014. A Novel Approach to Non-GPS Navigation Using Infrasound. Technical Paper #CSEE 14-03. [Online] September 11, 2014. https://www.eng.auburn.edu/files/acad_depts/csse/csse_technical_reports/csse1403.pdf.

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Christie, Edward Hunter. 2020. Artificial Intelligence at NATO: dynamic adoption, responsible use. NATO Review. [Online] November 24, 2020. https://www.nato.int/docu/review/articles/2020/11/24/artificial-intelligence-at-natodynamic-adoption-responsible-use/index.html.

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Funk, Jeffrey. 2019. AI and Hype: An Assessment of McKinsey’s Forecast for Artificial Intelligence. LinkedIn Pulse. [Online] January 10, 2019. https://www.linkedin.com/pulse/aihype-assessment-mckinseys-forecast-artificial-dr-jeffrey-funk.

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Golson, Jordan. 2014. A Military-Grade Drone That Can Be Printed Anywhere. Wired. [Online] September 16, 2014. https://www.wired.com/2014/09/military-grade-dronecan-be-printed-anywhere.

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Grigonis, Hillary. 2017. Google designed an object-recognition program that won’t need the internet. Digital Trends. [Online] June 15, 2017. https://www.digitaltrends.com/mobile/google-mobilenets-open-source/.

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Kolodny, Lora and Weaver, Darren. 2018. These drones can haul a 20-pound load for 500 miles and land on a moving target. [Online] May 26, 2018. https://www.cnbc.com/2018/05/26/volans-i-drones-can-haul-cargo-for-500-miles-andland-on-a-moving-ship.html.

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Mizokami, Kyle. 2017. Another Ukrainian Ammo Dump Goes Up in Massive Explosion. Popular Mechanics. [Online] September 27, 2017. [Cited: January 30, 2021.] https://www.popularmechanics.com/military/weapons/news/a28412/ukrainian-ammodump-explosion/.

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Rogoway, Tyler. 2016. Meet Israel’s ‘Suicide Squad’ of Self-Sacrificing Drones. The Warzone. [Online] August 8, 2016. [Cited: January 30, 2021.] http://www.thedrive.com/thewar-zone/4760/meet-israels-suicide-squad-of-self-sacrifcing-drones.

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Thompson, Mark. 2013. The Navy’s Amazing Ocean-Powered Underwater Drone. Time. [Online] December 22, 2013. [Cited: January 30, 2021.] https://swampland.time.com/2013/12/22/navy-underwater-drone/.

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Belot, Laure. 2018. Intelligence artificielle en Afrique: le risque de captation de valeur existe, decrypte Cedric Villani. Le Monde. [Online] June 17, 2018. ttps://www.lemonde.fr/afrique/article/2018/06/17/intelligence-artificielle-en-afrique-lerisque-de-captation-de-valeur-existe-decrypte-cedric-villani_5316644_3212.html..

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Bianculli, Tom. 2017. Digital Disruption is Challenging the Status Quo. InformationWeek. [Online] 2017. https://www.informationweek.com/strategic-cio/digital-business/digitaldisruption-is-challenging-the-status-quo/a/d-id/1328558?.

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Bloch, Ben. 2019. 4 Industries to watch for AI disruption. CIO. [Online] December 18, 2019. [Cited: February 12, 2021.] https://www.cio.com/article/3509962/4-industries-towatch-for-ai-disruption.html.

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Brunner, Richard. 2020. How Blockchain Technology will Impact our Legal System. Mondaq. [Online] February 14, 2020. [Cited: February 12, 2021.] https://www.mondaq.com/fin-tech/893706/how-blockchain-technology-will-impactour-legal-system.

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Ethical and Legal Challenges of Artificial Intelligence-Driven Healthcare. Gerke, Sara, Minssen, Timo and Cohen, Gienn. 2020. June 2020, Elsevier Public Health Emergency Collection.

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Explainable Artificial Intelligenc (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Arrieta, Alejandro Barredo, et al. 2020. s.l. : Elsevier, June 2020, Information Fusion, Vol. 58, pp. 82-115.

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How can International Law Regulate Autonomous Weapons? Piccone, Ted. 2018. s.l. : Brookings, 2018.

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Pauwels, Eleonore. 2018. The new geopolitics of artificial intelligence . World Economic Forum. [Online] October 15, 2018. [Cited: February 12, 2021.] https://www.weforum.org/agenda/2018/10/artificial-intelligence-ai-new-geopoliticsun/.

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Responsible Artificial Intelligence: Desiging for Human Values. Dignum, Virginia. 2017. Special Issue No. 1, September 25, 2017, ITU Journal: ICT Discoveries .

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Roe, David. 2018. 11 Industries Being Disrupted by AI. CMS Wire. [Online] April 27, 2018. [Cited: February 12, 2021.] https://www.cmswire.com/information-management/11industries-being-disrupted-by-ai/.

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Rugge, Fabio. 2019. The Global Race for Technological Superiority. Milan : s.n., 2019.

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Sallomi, Paul. 2015. Artificial Intelligence Goes Mainstream. Wall Street Journal (CIO Journal). [Online] 2015.

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Samal, Sameer and Shetty, Sameeksha. 2020. AI Diplomacy: Review Brief. Indian Strategy for AI & Law. 2020.

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Stankovic, Mirjana, et al. 2017. Exploring Legal, Ethical and Policy Implications of Artificial Intelligence. Law, Justice and Development. 2017.

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The geopolitics of artificial intelligence: The return of empires? Miailhe, Nicolas. 2018. 3, 2018, Politique Etrangere, pp. 105-117.

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• Bloustein, E. 1964. Privacy as an Aspect of Human Dignity: An Answer to Dean Prosser. s.l. : New York University Law Review, 1964. • Civil Liability for Artificial Intelligence: What Should its Basis Be? Boghetti, Jean Sebastien. 2019. June 2019, Revue Des Juristes De Sciences Po, pp. 94-102. • Comparing Surveillance Powers: UK, US and France. Murray, A.D. 2015. Special Issue, 2015, LSE Law Policy Briefing Papers. • DeCew and Judith. 2018. Privacy. Stanford Encyclopedia of Philosophy. [Online] 2018. [Cited: February 2, 2021.] https://plato.stanford.edu/archives/spr2018/entries/privacy/. • Electronic Frontier Foundation. International Privacy Standards. Electronic Frontier Foundation. [Online] [Cited: February 3, 2021.] eff.org. • Electronic Privacy Information Centre Washington, DC, USA. 2004. Privacy and Human Rights. An International Survey on Privacy Laws and Developments. London, UK: Privacy International, 2004. • European Parliament. 2020. Artificial Intelligence and Civil Liability. 2020. 978-92-8469108-4. • Harding, Luke. 2014. The Snowden Files. s.l. : Penguin Random House, 2014. • History.com. Deep Blue Defeats Garry Kasparov in Chess Match. History. [Online] www.history.com/this-day-in-history/deep-blue-defeats-garry-kasparov-in-chess-match. • Locke, John. 1689. Second Treatise on Civil Government. s.l. : Awnsham Churchill , 1689. • Mill, John Stuart. 1859. On Liberty. London : Longman, Roberts & Green, 1859. • Mozur, Paul. 2019. One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority. The New York Times. [Online] April 14, 2019. https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificialintelligence-racial-profiling.html. • Obscurity by Design. Hartzog, W. and Stutzman, F. 2013. 2, 2013, Washington Law Review, Vol. 88, p. 385. • Parent, W. 1983. "Privacy, Morality and the Law", Philosophy and Public Affairs. 1983. • Placing Blame. Moore, M. 1997. s.l.: Clarendon Press Oxford, 1997, pp. 595, 636. • Privacy as an International Human Right and the Right to Obscurity in Cyberspace. Rengel, Alexandra. 2, Groningen Journal of International Law, Vol. 2, pp. 33-54. • Privacy vs National Security. Jawaid, Tajdar. 2020. 7, July 2020, International Journal of Computer Trends and Technology, Vol. 68. • Purpose and Function Creep by Design: Transforming the Face of Surveillance through the Internet of Things. Wisman, T. 2013. 2013, European Journal of Law and Technology, Vol. 4.

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• Samal, Sameer. 2020. Machine Learning and its Privacy Implications in India: Analysis of the Logistical Imperative in Data Protection and Jurisprudence. Artificial Intelligence and Policy in India. s.l. : Indian Society of Artificial Intelligence and Law, 2020, Vol. 1. • Security and Privacy Considerations in Artificial Intelligence and Machine Learning . Prabhu, Manish. 2019. 2019. • Striking the Balance Between Privacy and Governance in the Age of Technology. Ran, Jing. 2016. 2016, SPICE | Philosophy, Politics and Economics Undergraduate Journal, Vol. 11, pp. 7-26. • Supreme Court of India. 2017. Justice K.S. Puttaswamy & Anr. v. Union of India. s.l. : Supreme Court of India, 2017. WP(C) 494 of 2012. • The Case for Online Obscurity. Hartzog, W. and Stutzman, F. 2013. 2013, California Law Review, Vol. 101, pp. 1-52.

International Intellectual Property Law • Artificial Intelligence: A Modern Approach. Norvig, S. Russell and P. 2016. s.l. : Pearson, 2016, Vol. 3rd. • Conversation on Intellectual Property (IP) and Artificial Intelligence (AI): Draft Issues Paper on Intellectual Property Policy and Artificial Intelligence. WIPO. 2019. s.l. : WIPO, 2019. • Joint Research Centre, Europe. 2018. Artificial Intelligence: A European Perspective. Europe : Joint Research Centre, 2018. • On the Impossibility of Intellectual Property. Wisniewski, Jakub Bozydar. 2020. s.l. : Mises Institute, 2020, Quarterly Journal of Austrian Economics. • R.Wexler. 2018. Life, Liberty and Trade Secrets: Intellectual Property in the Criminal Justice System. s.l. : Stanford Law Review, 2018. pp. 1343, 1373-74. • Rethinking the Development of Patents: An Intellectual History, 1550–1800. Mossoff, A. 2001. 2001, Hastings Law Journal, Vol. 52. • The Entrepreneur: Real and Imagined. Salerno, Joseph T. 2008. 2008, Quarterly Journal of Austrian Economics. • Turing, A.M. 1950. Computing Machinery and Intelligence. s.l. : Mind, 1950. pp. 433-460. Vols. LIX, Issue 236.

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International Cybersecurity Law Aligning the international protection of “the public core of the internet” with state sovereignty and national security. Broeders, D. 2017. 3, 2017, Journal of Cyber Policy, Vol. 2. Altexsoft.com. How to choose Fraud Detection Software: Features, Characteristics, Key Providers. Altexsoft.com. [Online] https://www.altexsoft.com/blog/business/how-tochoose-fraud-detection-software-features-characteristics-key-providers-/. Barlow, J. P. 1996. A declaration of the independence of cyberspace. [Online] 1996. https://www.eff.org/cyberspace-independence. Biersteker, T. 2012. State, sovereignty and territory. Handbook of International Relations. s.l. : SAGE Publications Ltd, 2012. Brundage, M., et al. 2018. The malicious use of artificial intelligence: Forecasting,

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2020 Handbook on Artificial Intelligence and International Law prevention, and mitigation. [Online] 2018. https://maliciousaireport.com/. Cowhey, P. and Aronson, J. 2017. Digital DNA. Oxford : Oxford University Press, 2017. Deterrence and norms to foster stability in cyberspace. Taddeo, M. 2018. 2018, Philosophy & Technology, Vol. 31. European Commission. 2019. Ethics Guidelines for trustworthy AI. European Commission. [Online] 2019. https://ec.europa.eu/futurium/en/ai-allianceconsultation/guidelines. Heinl, C. 2019. CBMs How to build trust and confidence in cyberspace? Lessons and good Practices-Cyber Direct Training. s.l. : European Institute of Security Studies/EU Cyber Direct, 2019. How AI can be a force for good. Taddeo, M. and Floridi, L. 2018. 6404, 2018, Science, Vol. 361. Hurrell, A. and Macdonald, T. 2012. Ethics and Norms in International Relations. Handbook of International Relations. s.l. : SAGE Publications Ltd, 2012. Kello, L. 2017. The virtual weapon and international order. New Haven : Yale University Press, 2017. Lessig, L. 2000. Code is law. Harvard Magazine. [Online] January 1, 2000. https://www.harvardmagazine.com/2000/01/code-is-law-html. —. 2006. Code: And other laws of cyberspace. s.l. : Basic Books, 2006. Lessons from the Montreal Protocol: Guidance for the next international climate change agreement. Green, B. 2009. 1, 2009, Environmental Law, Vol. 39. Maurer, T. 2018. Cyber Mercenaries: The State, hackers, and power. Cambridge : Cambridge University Press, 2018. ncbi.nlm.nih.gov. NHS Ransomware Attack spreads worldwide. ncbi.nlm.nih.gov. [Online] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5461132/. RedSeal. 2018. Hacking Our Security: Digital Resilience for the Next Cyber Threat, interview with Ray Rothrock. RedSeal. [Online] November 20, 2018. https://www.computerhistory.org/atchm/hacking-our-security-digital-resilience-for-thenext-cyber-threat. Timmers, P. 2019. Cybersecurity-Cyber direct training. s.l. : European Institute of Security Studies/EU Cyber Direct, 2019. —. 2019. Cybersecurity-Cyber direct training. s.l. : European Institute of Security Studies/EU Cyber Direct, 2019. UNIDIR. 2017. The weaponization of increasingly autonomous technologies: Autonomous weapon systems and cyber operations. UNIDIR Resources, No.7. UNIDIR. [Online] 2017.

International Telecommunication Law 2019. Architectural framework for machine learning in Future Networks including IMT-2020. s.l. : International Telecommunication Union, 2019. 2020. Case Study – Telecom. [Online] Singapore : Avanseus, 2020. CMS. 2021. CMS Expert Guide to 5G regulation and Law. CMS Law and Tax. [Online] 2021. https://cms.law/en/int/expert-guides/cms-expert-guide-to-5g-regulation-and-law. CRAWSHAW, James. AI/ML for CSP Operations: From Reactive to Predictive Autonomous. [Online] s.l. : TEOCO.

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DAHER, Tony. 2018. Cognitive management of self organized radio networks of fifth generation. s.l. : Research Gate, 2018. DELOITTE. The Age of Telecom Network Automation. [Online Article] s.l. : Telecom Engineering Centre of Excellence (TEE). GURNANEY, Tina. 2017. Why telcos will soon be betting on Artificial Intelligence to build their networks. [News Article] s.l. : Economic Times - Telecom, 2017. International Telecommunication Union. 2019. Architectural framework for machine learning in future networks including IMT-2020. International Telecommunication Union. [Online] June 2019. https://www.itu.int/rec/dologin_pub.asp?lang=e&id=T-RECY.3172-201906-I!!PDF-E&type=items. —. 1994. Final Acts of the Plenipotentiary Conference (Kyoto, 1994). International Telecommunication Union. [Online] 1994. http://search.itu.int/history/HistoryDigitalCollectionDocLibrary/4.15.43.en.100.pdf. —. 2020. Framework for data handling to enable machine learning in future networks including IMT-2020. International Telecommunication Union. [Online] February 2020. https://www.itu.int/rec/dologin_pub.asp?lang=e&id=T-REC-Y.3174-202002-I!!PDFE&type=items. —. 2018. Resolution 102 - ITU. International Telecommunication Union. [Online] 2018. https://www.itu.int/en/council/Documents/basic-texts/RES-102-E.pdf. —. 2018. Resolution 131 - ITU. International Telecommunication Union. [Online] 2018. https://www.itu.int/en/council/Documents/basic-texts/RES-131-E.pdf. —. 2018. Resolution 133 - ITU. International Telecommunication Union. [Online] 2018. https://www.itu.int/en/council/Documents/basic-texts/RES-133-E.pdf. —. 2018. Resolution 137 - ITU. International Telecommunication Union. [Online] 2018. https://www.itu.int/dms_pub/itu-s/md/19/cl/c/S19-CL-C-0144!!MSW-E.docx. —. 2018. Resolution 177 - ITU. International Telecommunication Union. [Online] 2018. https://www.itu.int/en/ITUD/Technology/Documents/ConformanceInteroperability/PP18_Res_177.pdf. —. 2014. Resolution 182 - ITU. International Telecommunication Union. [Online] 2014. https://www.itu.int/en/council/Documents/basic-texts/RES-182-E.pdf. —. 2010. Resolution 184 - ITU. International Telecommunication Union. [Online] 2010. https://www.itu.int/en/ITU-D/Digital-Inclusion/Doc/PP10%20RESOLUTION%20%20184-Indigenous.docx. —. 2018. Strengthening the regional presence (Res. 25) - ITU. [Online] 2018. https://www.itu.int/md/S17-CL-C-0025/en. —. 2005. Tunis Agenda for the Information Society (WSIS-05/TUNIS/DOC/6(Rev. 1)E). World Summit on the Information Society. [Online] November 18, 2005. https://www.itu.int/net/wsis/docs2/tunis/off/6rev1.html. LOHMULLE, Simon. 2020. Cognitive Self-Organizing Network Management for Automated Configuration of Self-Optimization SON Functions. [Thesis] 2020. NOKIA. Nokia AVA. Nokia. [Online] [Cited: January 12, 2021.] https://www.nokia.com/networks/solutions/nokia-ava/.

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2020 Handbook on Artificial Intelligence and International Law Online Learning for Energy Saving and Interference Coordination in HetNets. ROMERO, Jose Antonio Ayala, ALCARAZ, Juan J. and Michele, ZORZI. 2019. 1, s.l. : IEEE Journal on Selected Areas in Communications, 2019, Vol. 1. PENG, Yu, et al. 2020. Deep Reinforcement Learning Aided Cell Outage Compensation Framework in 5G Cloud Radio Access Networks. Springer Verlag. 2020, Vol. 1.

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ACAP Saint John. “Cloud Seeding”. ACAP Saint John. [online]. 2018. [01 September 2020]. Available from: <http://www.acapsj.org/cloud-seeding>. Ahaskar, Abhijit. “Artificial Intelligence, Machine Learning Chip in to Fight Climate Change, Protect Environment”. LiveMint. [online]. 30 January 2020. [01 September 2020]. Available from: <https://www.livemint.com/technology/tech-news/artificial-intelligenceml-chip-in-to-fight-climate-change-protect-environment-11580399748370.html>. Bassulto, Dominic. “Five Ways Technology Can Help Us Cope With Blizzards”. The Washington Post. [online]. 27 January 2015. [02 September 2020]. Available from: <https://www.washingtonpost.com/news/innovations/wp/2015/01/27/five-waystechnology-can-help-us-cope-with-blizzards/>. BBVA. “Artificial Intelligence and Green Algorithms Contribute to Improved Energy Efficiency at BBVA Headquarters”. BBVA. [online]. 09 October 2019. [01 September 2020]. Available from: <https://www.bbva.com/en/artificial-intelligence-and-greenalgorithms-contribute-to-improved-energy-efficiency-at-bbva-headquarters/>. Bodansky, Daniel et al. “International Environmental Law: Mapping the Field”. Oxford Handbooks Online. [online]. August 2008. [29 August 2020]. Available from: <https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780199552153.001.000 1/oxfordhb-9780199552153-e-1>. Cappucci, Matthew. “Using Facial Recognition Technology For… Hailstorms?”. The Washington Post. [online]. 22 August 2019. [02 September 2020]. Available from: <https://www.washingtonpost.com/weather/2019/08/22/using-facial-recognitiontechnology-hailstorms/>. Certain Activities Carried Out by Nicargua in the Border Area (Costa Rica v. Nicaragua) and Construction of a Road in Costa Rica Along the San Juan River (Nicaragua v. Costa Rica). ICJ Reports. 2015. p. 665. Chu, Jennifer. “Machine Learning Picks Out Hidden Vibrations From Earthquake Data”. MIT News. [online]. 28 February 2020. [01 September 2020]. Available from: <https://news.mit.edu/2020/machine-learning-picks-out-hidden-vibrations-earthquakedata-0228>. Cortes, Ulises et al. “Artificial Intelligence and Environmental Decision Support Systems”. Applied Intelligence. [online]. 2000. Volume 13, pp. 77-91. [01 September 2020]. Available from: <https://www.cs.upc.edu/~ia/articulos/UCortesetal-APIN-13-1-2000.pdf>. Davis, Vincy. “How Artificial Intelligence and Machine Learning Can Help Address Climate Change”. Packt. [online]. 16 September 2019. [01 September 2020]. Available from: <https://hub.packtpub.com/how-artificial-intelligence-and-machine-learning-can-helpus-tackle-the-climate-change-emergency/>. Davies, Ashley et al. “Artificial Intelligence in the NASA Volcano Sensorweb: Over a Decade in Operations”. California Institute of Technology. [online]. 2015. [02 September 2020].

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Available from: <https://ai.jpl.nasa.gov/public/documents/papers/davies_ijcai2015_nasa.pdf>. Dedezade, Esat. “Combating Drought With AI and the Cloud”. Microsoft: Artificial Intelligence. [online]. 20 June 2019. [02 September 2020]. Available from: <https://news.microsoft.com/europe/features/combating-drought-with-ai-and-thecloud/>. Deoras, Srishti. “Robotics, AI to Come Handy in Waste Management and Garbage Handling”. Analytics India Magazine: Opinions. [online]. 24 April 2017. [01 September 2020]. Available from: <https://analyticsindiamag.com/robotics-ai-come-handy-wastemanagement-garbage-handling/>. Deoras, Srishti. “Deep Learning Based Hurricane Intensify Estimator is Helping Track the Path of Hurricane Florence”. Analytics India Magazine. [online]. 14 September 2018. [02 September 2020]. Available from: <https://analyticsindiamag.com/deep-learning-basedhurricane-intensity-estimator-is-helping-track-the-path-of-hurricane-florence/>. Dol, Quinten. “Can Machine Learning Help Clean Up A Tornado’s Aftermath”. Builtin. [online]. 17 January 2020. [02 September 2020]. Available from: <https://builtin.com/machine-learning/machine-learning-tracking-tornado-damage>. Dubrova, Kateryna. “A Decade After Deepwater Horizon: IoT Tech to Predict Oil Spills.. Or Not?”. ABI Research. [online]. 16 January 2020. [01 September 2020]. Available from: <https://www.abiresearch.com/blogs/2020/01/16/decade-after-deepwater-horizon-iottech-predicting-oil-spills-or-not/>. Dupuy, Pierre- Marie et al. “Customary International Law and the Environment”. Cambridge Centre for Environment, Energy and Natural Resource Governance: Working Paper- 2. [online]. December 2018. [28 August 2020]. Available from: <https://www.ceenrg.landecon.cam.ac.uk/working-paperfiles/CEENRG_WP_19_CustomaryInternationalLawandtheEnvironment.pdf>. Economic Times. “Using AI, ML to Predict Drought”. Economic Times. [online]. 30 May 2019. [02 September 2020]. Available from: <https://economictimes.indiatimes.com/small-biz/startups/features/using-ai-ml-topredict-drought/dry-lands/slideshow/69578945.cms>. EhsAI. “Welcome to ehsAI”. ehsAI. [online]. 2020. [01 September 2020]. Available from: <https://www.ehsai.ca>. Enviance. “How to Lower Your EHS Compliance Costs With AI”. Enviance: Cority. [online]. 15 July 2019. [01 September 2020]. Available from: <https://www.enviance.com/ehsinsider/how-to-lower-your-ehs-compliance-costs-with-ai>. Farmen, Nicholas. “How AI is Helping Solve Climate Change”. Smashing Magazine. [online]. 19 September 2019. [01 September 2020]. Available from: <https://www.smashingmagazine.com/2019/09/ai-climate-change/>. Fawal, Ninar. “Landslides: Identifying Landslide Risk Areas”. StoryMaps. [online]. 29 November 2019. [02 September 2020]. Available from: <https://storymaps.arcgis.com/stories/840ba58608f545999e5a7e46b4e5ea7e>. Fujitsu. “Fujitsu Leverages AI Tech in Joint Project to Contribute to Safe Tsunami Evacuation in Kawasaki”. Fujitsu. [online]. 24 October 2019. [02 September 2020]. Available from: <https://www.fujitsu.com/global/about/resources/news/pressreleases/2019/1024-01.html>. Fuller Thomas and Metz Cade. “AI is Helping Scientists Predict When and Where the Next Big Earthquake Will Be”. The New York Times. [online]. 26 October 2018. [02 September 2020]. Available from: <https://www.nytimes.com/2018/10/26/technology/earthquakepredictions-artificial-intelligence.html>.

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2020 Handbook on Artificial Intelligence and International Law Ganjoo, Shweta. “Google is Using AI to Monitor Flood in India, Pilot Project Patna Called a Success”. India Today. [online]. 15 July 2019. [02 September 2020]. Available from: <https://www.indiatoday.in/technology/news/story/google-is-using-ai-to-monitorflood-in-india-pilot-project-patna-called-a-success-1569345-2019-07-15>. Greenman, Simon. “How Can AI Help Tackle Climate Change?”. Towards Data Science. [online]. 06 December 2019. [01 September 2020]. Available from: <https://towardsdatascience.com/how-can-technology-and-artificial-intelligence-helptackle-climate-change-b97db0ff4c95>. Herweijer, Celine and Waughray, Dominic. “Harnessing Artificial Intelligence for the Earth”. World Economic Forum, PricewaterhouseCoopers and Stanford Woods Institute for the Environment: Fourth Industrial Revolution for the Earth Series. [online]. January 2018. [29 August 2020]. Available from: <http://www3.weforum.org/docs/Harnessing_Artificial_Intelligence_for_the_Earth_re port_2018.pdf>. How, Yaz. “Using Artificial Intelligence to Achieve Zero Waste”. Topbots. [online]. 15 April 2019. [01 September 2020]. Available from: <https://www.topbots.com/ai-zero-wastecase-study/>. IBM. “The World’s Most Accurate Forecast Just Got Better”. IBM. [online]. 2020. [01 September 2020]. Available from: <https://www.ibm.com/weather/industries/crossindustry/graf>. IBM100. “Deep Thunder”. IBM100. [online]. 2020. [01 September 2020]. Available from: <https://www.ibm.com/ibm/history/ibm100/us/en/icons/deepthunder/>. Ilchenko, Valeriy. “5 Great Examples of Sustainable Technology Implementation”. ByteAnt. [online]. 27 July 2020. [01 September 2020]. Available from: <https://www.byteant.com/blog/5-great-examples-of-sustainable-technologyimplementation/>. International Telecommunication Union. “United Nations Activities on Artificial Intelligence (AI)”. [online]. 2019. [04 September 2020]. Available from: <https://www.itu.int/dms_pub/itu-s/opb/gen/S-GEN-UNACT-2019-1-PDF-E.pdf>. Jeffrey, Jonathan. “8 Companies Utilizing AI to Tackle Climate Change”. Entrepreneur India. [online]. 27 September 2019. [01 September 2020]. Available from: <https://www.entrepreneur.com/article/340002>. Joshi, Naveen. “4 Ways AI Can Revolutionize Waste Management”. Allerin. [online]. 04 October 2018. [01 September 2020]. Available from: <https://www.allerin.com/blog/4ways-ai-can-revolutionize-waste-management>. Joshi, Naveen. “How AI Can and Will Predict Disasters”. Forbes. [online]. 15 March 2019. [02 September 2020]. Available from: <https://www.forbes.com/sites/cognitiveworld/2019/03/15/how-ai-can-and-willpredict-disasters/#549bb1125be2>. Kanowitz, Stephanie. “AI May Be Able to Predict Urban Landslides”. GCN Resource Center. [online]. 04 March 2020. [02 September 2020]. Available from: <https://gcn.com/articles/2020/03/04/ai-landslide-prediction.aspx>. Karlsruhe Institute of Technology. “Artificial Intelligence Improves Seismic Analyses”. Phys. [online]. 14 June 2019. [02 September 2020]. Available from: <https://phys.org/news/2019-06-artificial-intelligence-seismic-analyses.html>. Kesari, Ganes. “How AI is Helping Combat Deforestation”. The Sociable: Technology. [online]. 25 March 2019. [01 September 2020]. Available from: <https://sociable.co/technology/how-ai-helping-combat-deforestation/>.

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Khan, M. Feroz and Pannikar, Preetha. “Application of Expert Systems for Environmental Impact Assessment of Reservoirs”. Scribd. [online]. 2007. [01 September 2020]. Available from: <https://www.scribd.com/document/44883288/Artificial-Intelligence>. Law Library of Congress. “Regulation of Artificial Intelligence in Selected Jurisdictions”. Law Library of Congress. [online]. January 2019. [04 September 2020]. Available from: <https://www.loc.gov/law/help/artificial-intelligence/regulation-artificialintelligence.pdf>. Malliaraki, Eirini. “Towards Intelligent Green and Blue Infrastructure”. Medium. [online]. 11 January 2020. [01 September 2020]. Available from: <https://medium.com/@eirinimalliaraki/towards-intelligent-green-and-blueinfrastructures-52cab0f502b5>. Marsman, Jennifer. “AI For Earth: Using Machine Learning to Monitor, Model and Manage Natural Resources.” Artificial Intelligence Conference. [online]. 2018. [31 August 2020]. Available from: <https://conferences.oreilly.com/artificial-intelligence/ai-ca2018/public/schedule/detail/68568.html>. Mathur, Shivin. “International Environmental Law and its Scope and Implementation in India”. Institute of Law Nirma University: Articles. [online]. 20 June 2016. [28 August 2020]. Available from: <http://ilnu-jcel.org/international-environmental-law-and-its-scope-andimplementation-in-india/>. Microsoft Reporter. “Saving the Seas: how AI is Helping to Protect Our Oceans”. Microsoft: AI. [online]. 02 July 2019. [01 September 2020]. Available from: <https://news.microsoft.com/europe/features/saving-the-seas-how-ai-is-helping-toprotect-our-oceans/>. Mohapatra, Pragna. “Artificial Intelligence is Able to Predict Storms and Cyclones.” IndustryWired. [online]. 24 July 2019. [02 September 2020]. Available from: <https://industrywired.com/artificial-intelligence-is-able-to-predict-storms-andcyclones/>. Moltzau, Alex. “Artificial Intelligence and Forest Management”. ODSC Journal. [online]. (04 September 2019). [01 September 2020]. Available from: <https://medium.com/odscjournal/artificial-intelligence-and-forest-management50f480b56325>. Muraleedharan, Sarath. “Role of Artificial Intelligence in Environmental Sustainability”. EcoMENA. [online]. 06 March 2018. [31 August 2020]. Available from: <https://www.ecomena.org/artificial-intelligence-environmental-sustainability/>. National Academies Press. “Leveraging Artificial Intelligence and Machine Learning to Advance Environmental Health Research and Decisions: Proceedings of A Workshop in Brief”. National Academies Press. [online]. August 2019. [01 September 2020]. Available from: <https://www.nap.edu/read/25520/chapter/1>. Nielsen, Michael. “Robots With Artificial Intelligence to Sort Hazardous Waste”. Danish Technological Institute. [online]. 2020. [01 September 2020]. Available from: <https://www.dti.dk/specialists/robots-with-artificial-intelligence-to-sort-hazardouswaste/38310>. NSSL. “Severe Weather 101: Tornado Detection”. National Severe Storms Laboratory. [online]. 2020. [02 September 2020]. Available from: <https://www.nssl.noaa.gov/education/svrwx101/tornadoes/detection/>. Osumi, Magdalena. “How AI Will Help Us Better Understand Tsunami Risks”. PreventionWeb. [online]. 16 August 2019. [02 September 2020]. Available from: <https://www.preventionweb.net/news/view/67242>.

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2020 Handbook on Artificial Intelligence and International Law Padma, Preeti. “How is AI Empowering the Weather Forecasting Technology?”. Analytics Insight. [online]. 01 June 2020. [02 September 2020]. Available from: <https://www.analyticsinsight.net/ai-empowering-weather-forecasting-technology/>. Ponce De Leon, Sandra. “Can AI Save Our Oceans? Let’s Start With the Data”. Forbes. [online]. 17 September 2019. [01 September 2020]. Available from: <https://www.forbes.com/sites/cognitiveworld/2019/09/17/can-ai-save-our-oceanslets-start-with-the-data/#198cde65700d>. Reilly, Michael. “This Robot Will Sail for Months on the Lookout for a Tsunami”. MIT Technology Review. [online]. 30 January 2017. [03 September 2020]. Available from: <https://www.technologyreview.com/2017/01/30/154301/this-robot-will-sail-formonths-on-the-lookout-for-a-tsunami/>. Robinson, Nicholas. “Environmental Law: Is an Obligation Erga Omnes Emerging?”. International Union for Conservation of Nature. [online]. 04 June 2018. [28 August 2020]. Available from: <https://www.iucn.org/sites/dev/files/content/documents/2018/environmental_law_is _an_obligation_erga_omnes_emerging_interamcthradvisoryopinionjune2018.pdf>. Sanu, Maria. “5 Examples of How AI is Helping Companies Become More Sustainable”. Winnow. [online]. 23 July 2019. [01 September 2020]. Available from: <https://blog.winnowsolutions.com/5-examples-of-how-ai-is-helping-companiesbecome-more-sustainable>. ScienceDaily. “‘Artificial Intelligence’ Fit to Monitor Volcanoes: Platform Uses ‘Machine Learning’ to Analyse Satellite Data” ScienceDaily. [online]. 15 July 2019. [02 September 2020]. Available from: <https://www.sciencedaily.com/releases/2019/07/190715103313.htm>. Sea Machines. “Autonomous Vessel Technology Enhances Oil- Spill Response and Recovery Efforts”. Sea Machines. [online]. 10 May 2018. [01 September 2020]. Available from: <https://sea-machines.com/autonomous-vessel-technology-enhances-oil-spillresponse-and-recovery-efforts>. Sea Machines. “Products”. Sea Machines. [online]. 2020. [01 September 2020]. Available from: <https://sea-machines.com/products>. Skrabania, Lydia. “Artificial Intelligence and Drones Join the Fight to Save the Rainforest”. Reset. [online]. 03 March 2020. [01 September 2020). Available from: <https://en.reset.org/blog/artificial-intelligence-and-drones-join-fight-save-rainforest03032020>. Snow, Jackie. “Can Artificial Intelligence Help Save the Natural World?” PBS. [online]. 21 March 2019. [31 August 2020]. Available from: <https://www.pbs.org/wgbh/nova/article/can-artificial-intelligence-help-save-thenatural-world/>. Soto, Max Valverde. “General Principles of International Environmental Law”. International Law Students Association Journal of International and Comparative Law. [online]. 1996. Volume 3, pp. 193-209. [28 August 2020]. Available from: <https://nsuworks.nova.edu/cgi/viewcontent.cgi?article=1069&context=ilsajournal>. The Guardian Labs. “AI Could be a Critical Tool to Help Save the Planet”. The Guardian Labs: AI for Earth. [online]. 30 April 2019. [01 September 2020]. Available from: <https://www.theguardian.com/ai-for-earth/2019/apr/30/ai-tech-sustainable-planet>. Trail Smelter Case (United States of America v. Canada). [online]. Arbitral award dated 16 April 1938 and 11 March 1941. [28 August 2020]. Available from: <https://legal.un.org/riaa/cases/vol_III/1905-1982.pdf>.

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Tyers, Pamela. “AI Relieves Data Drought For Farmers”. CSIROscope. [online]. 26 May 2020. [02 September 2020]. Available from: <https://blog.csiro.au/ai-relieves-datadrought-for-farmers/>. United Nations and International Telecommunication Union. “Frontier Technologies to Protect the Environment and Tackle Climate Change”. United Nations. [online]. 2020. [01 September 2020]. Available from: <https://www.itu.int/en/action/environment-andclimate-change/Documents/frontier-technologies-to-protect-the-environment-andtackle-climate-change.pdf>. University of Sheffield. “Using Artificial Intelligence to Reduce Urban Flooding”. University of Sheffield: Engineering at Sheffield. [online]. 2020. [02 September 2020]. Available from: <https://www.sheffield.ac.uk/engineering/about/business/using-artificial-intelligencereduce-urban-flooding>. UN-SPIDER. “New Studies Use Ground Based Satellite and AI Technologies to Improve Volcanic Eruption Forecasting”. UN-SPIDER. [online]. 02 June 2020. [02 September 2020]. Available from: <http://www.un-spider.org/news-and-events/news/new-studies-useground-based-satellite-and-ai-technologies-improve-volcanic>. USC CAIS. “The Symposium on AI for Conservation Provides a Forum to Explore How AI can Protect Our Wildlife, Forests and Fish”. University of Southern California Centre for Artificial Intelligence in Society: Blog Post. [online]. 21 February 2019. [31 August 2020]. Available from: <https://www.cais.usc.edu/news/how-artificial-intelligence-can-protect-theenvironment-and-contribute-to-conservation-efforts>. UIHI Lab. “Flood AI: Artificial Intelligence for Flood Preparedness”. UIHI Lab: Projects. [online]. 2020. [02 September 2020]. Available from: <https://hydroinformatics.uiowa.edu/projects/projects_floodai.php>. Verma, S.K. An Introduction to Public International Law. 2nd Edition. Mumbai: Satyam Law International. 2012. ISBN: 978-8192120416. Vinuesa, Ricardo et al. “The Role of Artificial Intelligence in Achieving the Sustainable Development Goals”. Nature Communications. [online]. 13 January 2020. Volume 11, No. 233. [29 August 2020]. Available from: <https://www.nature.com/articles/s41467-01914108-y>. Wahyono, Irawan et al. “New Method of Artificial Intelligence for Disaster Information Floods Use Distributed Wireless Sensors”. IEEE Explorer. [online]. 28 October 2019. [02 September 2020]. Available from: <https://ieeexplore.ieee.org/abstract/document/8884315>. International Energy Law

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ABB. “Harnessing the Power of Water”. ABB. [online]. (2020). [26 October 2020]. Available from: <https://new.abb.com/power-generation/segments/hydro-power>. Alford, Roger et al. “The Three Hottest Energy Arbitrations of 2017”. Kulwer Arbitration Blog. [online]. (04 January 2018). [24 October 2020]. Available from: <http://arbitrationblog.kluwerarbitration.com/2018/01/04/three-hottest-energyarbitrations-2017/>. Axpo. “Tailor- Made Trading Strategies, Increasing Returns”. Axpo. [online]. (03 December 2019). [25 October 2020]. Available from: <https://www.axpo.com/it/en/home/newsand-media/magazine.detail.html/magazine/renewable-energy/tailor-made-tradingstrategies-increasing-returns.html>.

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2020 Handbook on Artificial Intelligence and International Law Bese, Mark. “Artificial Intelligence in Mining, Oil & Gas and Agriculture”. Dassault Systemes. [online]. (16 March 2018). [31 August 2020]. Available from: <https://blogs.3ds.com/perspectives/ai-and-the-future-of-natural-resourcesindustries/>. Biofuels International. “Lanzatech Looks to AI to Further the Sustainable Bioeconomy”. Biofuels International. [online]. (24 January 2018). [26 October 2020]. Available from: <https://biofuels-news.com/news/lanzatech-looks-to-ai-to-further-the-sustainablebioeconomy/>. Bloomberg Law. “Vestas Buys Artificial Intelligence to Boost Wind Power”. Bloomberg Law. [online]. (05 February 2018). [26 October 2020]. Available from: <https://news.bloomberglaw.com/environment-and-energy/vestas-buys-artificialintelligence-to-boost-wind-power>. Bruce, Stuart. “International Energy Law”. Oxford Public International Law. [online]. (October 2014). [24 October 2020]. Available from: <https://opil.ouplaw.com/view/10.1093/law:epil/9780199231690/law-9780199231690e2143>. Bruce, Stuart. “International Law and Renewable Energy: Facilitating Sustainable Energy For All?”. Melbourne Journal of International Law. [online]. (July 2013). Volume 14. [24 October 2020]. Available from: <https://law.unimelb.edu.au/__data/assets/pdf_file/0011/1687439/02Bruce1.pdf>. Burlington Resources Inc. v. Republic of Ecuador. International Centre for Settlement of Investment Disputes Case No. ARB/08/5. [online]. (14 December 2012). [24 October 2020]. Available from: <https://www.italaw.com/sites/default/files/casedocuments/italaw1094_0.pdf>. Carnegie Mellon University. “Energy and Biofuels”. Carnegie Mellon University. [online]. (2020). [26 October 2020]. Available from: <https://www.cheme.engineering.cmu.edu/research/energy-biofuels.html>. Chen, James. “Energy Sector”. Investopedia. [online]. (31 March 2020). [24 October 2020]. Available from: <https://www.investopedia.com/terms/e/energy_sector.asp>. Clancy, Heather. “Get Ready For Virtual Power Plants”. Greenbiz. [online]. (17 January 2017). [25 October 2020]. Available from: <https://www.greenbiz.com/article/get-readyvirtual-power-plants>. Conger, Red et al. “Inside A Mining Company’s AI Transformation”. McKinsey & Co. [online]. (05 February 2020). [26 October 2020]. Available from: <https://www.mckinsey.com/industries/metals-and-mining/how-we-help-clients/insidea-mining-companys-ai-transformation>. Cornell University. “AI Helps Reduce Amazon Hydropower Dams’ Carbon Footprint”. ScienceDaily. [online]. (19 September 2019). [26 October 2020]. Available from: <https://www.sciencedaily.com/releases/2019/09/190919134703.htm>. Cornelissen, Arnoud. “Artificial Intelligence Helps Optimize Windmill Safety and Energy Generation”. Innovation Origins. [online]. (27 August 2020). [26 October 2020]. Available from: <https://innovationorigins.com/artificial-intelligence-helps-optimize-windmillsafety-and-energy-generation/>. Declaration of the United Nations Conference on the Human Environment (Stockholm Declaration). [online]. (1972). [24 October 2020]. Available from: <https://www.soas.ac.uk/cedep-demos/000_P514_IEL_K3736Demo/treaties/media/1972%20Stockholm%201972%20%20Declaration%20of%20the%20United%20Nations%20Conference%20on%20the%20 Human%20Environment%20-%20UNEP.pdf>.

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Deloitte. “Future of Mining With AI”. Deloitte. [online]. (2020). [26 October 2020]. Available from: <https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Energyand-Resources/deloitte-norcat-future-mining-with-ai-web.pdf>. Energy Charter Treaty. [online]. (1998). [24 October 2020]. Available from: <https://www.energycharter.org/fileadmin/DocumentsMedia/Legal/ECTC-en.pdf>. Energy Startups. “Top 57 Smart Grid Startups”. Energy Startups. [online]. (10 October 2020). [25 October 2020]. Available from: <https://www.energystartups.org/top/smartgrid/>. Evans, Richard and Gao, Jim. “DeepMind AI Reduces Google Data Centre Cooling Bill by 40%”. DeepMind. [online]. (20 July 2016). [26 October 2020]. Available from: <https://deepmind.com/blog/article/deepmind-ai-reduces-google-data-centre-coolingbill-40>. Framatome. “Framatome Partners With ADAGOS to Bring Artificial Intelligence to the Nuclear Energy Industry”. Framatome. [online]. (03 September 2020). [26 October 2020]. Available from: <https://www.framatome.com/EN/businessnews-1974/framatomepartners-with-adagos-to-bring-artificial-intelligence-to-the-nuclear-energy-industry.html>. Frye, Christopher. “4 Wyas Artificial Intelligence is Powering the Energy Industry”. The Kolabtree Blog. [online]. (05 November 2018). [25 October 2020]. Available from: <https://www.kolabtree.com/blog/4-ways-artificial-intelligence-is-powering-the-energyindustry/>. Gentry, Caroline. “Nowcasting Solar Electricity Generation With AI”. Engerati. [online]. (14 February 2020). [26 October 2020]. Available from: <https://www.engerati.com/artificialintelligence/nowcasting-solar-electricity-generation-with-ai/>. Heffron, Raphael et al. “A Treatise For Energy Law”. Journal of World Energy Law & Business. [online]. (March 2018). Volume 11, Issue 1. [24 October 2020]. Available from: <https://academic.oup.com/jwelb/article/11/1/34/4792991>. Horwitz, Lauren. “Smart Grid Security Will Get Boost From AI and 5G”. Internet of Things World Today. [online]. (14 July 2020). [25 October 2020]. Available from: <https://www.iotworldtoday.com/2020/07/14/smart-grid-security-will-get-boost-fromai-and-5g/>. Inovia AI. “Bringing A Hydro Plant From the 60s to Full AI- Driven IoT in One Step”. Inovia AI. [online]. (2020). [26 October 2020]. Available from: <https://inoviagroup.se/bringing-a-hydro-plant-from-the-60s-to-full-ai-driven-iot-inone-step/>. Jones, Jonathan. “Real- Time Predictive AI For Smart Grids”. Smart Energy International. [online]. (08 October 2020). [24 October 2020]. Available from: <https://www.smartenergy.com/industry-sectors/energy-grid-management/real-time-predictive-ai-for-smartgrids/>. Jouault, Fleur. “Applying Artificial Intelligence in Solar Energy”. New Energy Solar. [online]. (25 July 2019). [26 October 2020]. Available from: <https://www.newenergysolar.com.au/renewable-insights/renewable-energy/applyingartificial-intelligence-in-solar-energy>. Jungblut, Sarah- Indra. “In Germany, Artificial Intelligence is Making Wind Turbines More Bird- Friendly”. Reset. [online]. (16 February 2020). [26 October 2020]. Available from: <https://en.reset.org/blog/germany-artificial-intelligence-making-wind-turbines-morebird-friendly-02162020>. Karatzoglou, Ioanna. “Smart Mining: How Artificial Intelligence Can Benefit the Mining Industry”. PreScouter. [online]. (August, 2020). [26 October 2020]. Available from: <https://www.prescouter.com/2020/08/smart-mining-how-artificial-intelligence-canbenefit-the-mining-industry/>.

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2020 Handbook on Artificial Intelligence and International Law Kim, Kibaek et al. “Artificial Intelligence Can Make the U.S. Electric Grid Smarter”. Argonne National Laboratory. [online]. (14 June 2019). [25 October 2020]. Available from: <https://www.anl.gov/article/artificial-intelligence-can-make-the-us-electric-gridsmarter>. Kimani, Alex. “3 Ways Artificial Intelligence is Transforming the Energy Industry”. OilPrice. [online]. (02 September 2020). [26 October 2020]. Available from: <https://oilprice.com/Energy/Energy-General/3-Ways-Artificial-Intelligence-IsTransforming-The-Energy-Industry.html>. Kram, Jason. “Artificial Intelligence: Smart(er) Front and Back Office Energy Operations”. T&D World. [online]. (29 October 2019). [25 October 2020]. Available from: <https://www.tdworld.com/utility-business/article/20973320/artificial-intelligencesmarter-front-and-back-office-energy-operations>. Kyoto Protocol to the United Nations Framework Convention on Climate Change. FCCC/CP/1997/L.7/Add.1 [online]. (10 December 1997). [24 October 2020]. Available from: <https://unfccc.int/sites/default/files/resource/docs/cop3/l07a01.pdf>. Makala, Baloko and Bakovic, Tonci. “Artificial Intelligence in the Power Sector”. Emerging Markets Compass. [online]. (April 2020). [24 October 2020]. Available from: <https://www.ifc.org/wps/wcm/connect/bd3a196d-a88f-45af-bbc6e0b00790fba8/EMCompass_Note_81-05-web.pdf?MOD=AJPERES&CVID=n72pj5g>. McMahon, Jeff. “Thanks to Renewables and Machine Learning, Google Now Forecasts the Wind”. Forbes. [online]. (31 May 2020). [26 October 2020]. Available from: <https://www.forbes.com/sites/jeffmcmahon/2020/05/31/thanks-to-renewables-andmachine-learning-google-now-forecasts-the-wind/#4669b7f61865>. Microsoft. “Project Natick”. Microsoft. [online]. (2020). [26 October 2020]. Available from: <https://natick.research.microsoft.com>. Nair, Santosh. “The Growing Importance of AI in the Oil and Gas Industry”. GEP. [online]. (14 April 2020). [26 October 2020]. Available from: <https://www.gep.com/blog/mind/the-growing-importance-of-ai-in-the-oil-and-gasindustry>. Nhede, Nicholas. “Australia’s First AI- Enabled Wind Farm”. Smart Energy International. [online]. (19 September 2019). [26 October 2020]. Available from: <https://www.smartenergy.com/renewable-energy/australias-first-ai-enabled-wind-farm/>. Paris Agreement. [online]. (2015). [24 October 2020]. Available from: <https://unfccc.int/files/essential_background/convention/application/pdf/english_par is_agreement.pdf>. PJSC Ukrnafta, Stabil LLC and the Russian Federation. Permanent Court of Arbitration. [online]. (2019). [24 October 2020]. Available from: <https://www.italaw.com/sites/default/files/case-documents/italaw10481.pdf>. Power Engineering International. “Voith Utilizes Big Data and AI For Hydro Project.” Power Engineering International. [online]. (08 May 2018). [26 October 2020]. Available from: <https://www.powerengineeringint.com/digitalization/big-data/voith-utilizes-big-dataand-ai-for-hydro-project/>. Rabbitte, Pat. “Enabling Surplus Energy Trading Through AI”. 150Sec. [online]. (20 June 2019). [25 October 2020]. Available from: <https://150sec.com/enabling-surplus-energytrading-through-ai/11617/>. Rio Declaration on Environment and Development. A/CONF.151/26 (Vol. I). [online]. (1992). [24 October 2020]. Available from: <https://www.un.org/en/development/desa/population/migration/generalassembly/do cs/globalcompact/A_CONF.151_26_Vol.I_Declaration.pdf>.

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Sagar, Ram. “AI For Greener Good: Solar Panels Too Can Benefit From AI”. Analytics India Mag. [online]. (13 August 2019). [26 October 2020]. Available from: <https://analyticsindiamag.com/ai-for-greener-good-solar-panels-too-can-benefit-fromai/>. Sapp, Meghan. “Spanish Researchers Use AI to Predict Biodiesel Composition From Engine Noise”. Biofuels Digest. [online]. (16 May 2018). [26 October 2020]. Available from: <https://www.biofuelsdigest.com/bdigest/2018/05/16/spanish-researchers-use-ai-topredict-biodiesel-composition-from-engine-noise/>. Schmelzer, Ron. “AI Helping Extract Value in the Mining Industry”. Forbes. [online]. (09 August 2019). [31 August 2020]. Available from: <https://www.forbes.com/sites/cognitiveworld/2019/08/09/ai-helping-extract-valuein-the-mining-industry/#286cca470063>. Sennaar, Kuma. “Artificial Intelligence in Oil and Gas: Comparing the Application of 5 Oil Giants”. Emerj. [online]. (18 February 2019). [26 October 2020]. Available from: <https://emerj.com/ai-sector-overviews/artificial-intelligence-in-oil-and-gas/>. Sennaar, Kuma. “Artificial Intelligence For Energy Efficiency and Renewable Energy: 6 Current Applications”. Emerj. [online]. (09 July 2019). [26 October 2020]. Available from: <https://emerj.com/ai-sector-overviews/artificial-intelligence-for-energy-efficiency-andrenewable-energy/>. Sharma, Ayushee. “The Role of AI in the Renewable Energy Sector”. Electronicsb2b. [online]. (21 September 2020). [26 October 2020]. Available from: <https://www.electronicsb2b.com/important-sectors/solar-renewable-energy-inindia/the-role-of-ai-in-the-renewable-energy-sector/>. Singh, Jyoti. “IIT Hyderabad Researchers Use AI to Study Supply Chain Network of Biofuels”. The Federal. [online]. (04 July 2020). [26 October 2020]. Available from: <https://thefederal.com/news/iit-hyderabad-researchers-use-ai-to-study-supply-chainnetwork-of-biofuels/>. Sozontov, Andrey et al. “Implementation of Artificial Intelligence in the Electric Power Industry”. Energy Systems Research. [online]. (2019). [26 October 2020]. Available from: <https://www.e3sconferences.org/articles/e3sconf/pdf/2019/40/e3sconf_esr2019_01009.pdf>. Stark, Tina. “Machine Learning For Condition Monitoring in Hydropower Plants Using A Neural Network”. Lulea University of Technology: Dissertation. [online]. (2019). [26 October 2020]. Available from: <https://www.divaportal.org/smash/get/diva2:1337399/FULLTEXT01.pdf>. Sulikowski, Marcin. “Smart Grid- AI at the Service of the Power Distribution Network”. Naturaily. [online]. (28 June 2019). [25 October 2020]. Available from: <https://naturaily.com/blog/smart-grid-ai-in-power-distribution-network>. Sustainable Energy For All. “SEforALL Analysis of SDG 7 Progress- 2020”. Sustainable Energy For All. [online]. (2020). [24 October 2020]. Available from: <https://www.seforall.org/data-stories/seforall-analysis-of-sdg7-progress-2020>. TNO, “Sector Coupling: The Engine of a CO2- Free Society”. TNO. [online]. (2020). [25 October 2020]. Available from: <https://www.tno.nl/en/focus-areas/energytransition/roadmaps/towards-a-reliable-affordable-and-fair-energy-system/sectorcoupling-the-engine-of-a-co2-free-society/>. Uhrig, Robert. “Use of Artificial Intelligence to Enhance the Safety of Nuclear Power Plants”. Oak Ridge National Laboratory Report. [online]. (1989). [26 October 2020]. Available from: <https://www.osti.gov/servlets/purl/6953560>.

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2020 Handbook on Artificial Intelligence and International Law Uhrig, Robert and Hines, Wesley. “Integration of Artificial Intelligence Systems For Nuclear Power Plant Surveillance and Diagnostics”. Department of Nuclear Engineering Report. [online]. (2000). [26 October 2020]. Available from: <https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.36.717&rep=rep1&type=p df>. United Nations Framework Convention on Climate Change. [online]. (1992). [24 October 2020]. Available from: <https://unfccc.int/files/essential_background/background_publications_htmlpdf/appli cation/pdf/conveng.pdf>. Wawryk, Alexandra. “International Energy Law: An Emerging Academic Discipline”. Law as Change: Engaging With the Life and Scholarship of Adrian Bradbrook. [online]. (2014). [24 October 2020]. Available from: <https://www.jstor.org/stable/10.20851/j.ctt1sq5xcn.14?seq=7#metadata_info_tab_con tents>. Wehus, Walter. “Soon, Artificial Intelligence Can Operate Hydropower Plants”. University of Agder. [online]. (30 January 2017). [26 October 2020]. Available from: <https://www.uia.no/en/news/soon-artificial-intelligence-can-operate-hydropowerplants>. Yukos Universal Limited (Isle of Man) and the Russian Federation. Permanent Court of Arbitration Case No. AA 227. [online]. (18 July 2014). [24 October 2020]. Available from: <https://www.italaw.com/sites/default/files/case-documents/italaw3279.pdf>. Yurman, Dan. “AI Software to Improve Nuclear Reactor Designs, Performance, Safety, Lifetimes”. EnergyPost. [online]. (31 March 2020). [26 October 2020]. Available from: <https://energypost.eu/ai-software-to-improve-nuclear-reactor-designs-performancesafety/>. Zaremba, Haley. “Can Artificial Intelligence Save the Nuclear Industry?”. Oil Price. [online]. (22 June 2019). [26 October 2020]. Available from: <https://oilprice.com/Energy/EnergyGeneral/Can-Artificial-Intelligence-Save-The-Nuclear-Industry.html>. International Space Law

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AI Trends. “AI in Space: NASA Studying Exoplanets, ESA Supporting Satellites”. AI Trends. [online]. 30 April 2020. [23 December 2020]. Available from: <https://www.aitrends.com/ai-and-space-exploration/ai-in-space-nasa-studyingexoplanets-esa-supporting-satellites/>.

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Allioux, Renaud. “AI Products and Remote Sensing: Yes, It is Hard and Yes, You Need a Good Infra”. Medium. [online]. 24 July 2018. [22 December 2020]. Available from: <https://medium.com/earthcube-stories/ai-products-and-remote-sensing-yes-it-is-hardand-yes-you-need-a-good-infra-4b5d6cf822f1>.

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Bubenik, Travis. “Mars Rover Mission Will Use AI to Help Scientists Sort Out Findings”. Courthouse News Service. [online]. 25 June 2020. [22 December 2020]. Available from: <https://www.courthousenews.com/mars-rover-mission-will-use-ai-to-help-scientistssort-out-findings/>.

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Cepelka, Cestmir. “The Application of General International Law in Outer Space”. Journal of Air Law and Commerce. Volume 36, Issue 1. [online]. 1970. [21 December 2020]. Available from: <https://core.ac.uk/download/pdf/147639066.pdf>.

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Exploration Rover Mission”. AI in Space: IEEE Computer Society. [online]. 2004. [22 December 2020]. Available from: <https://ti.arc.nasa.gov/m/pubarchive/542h/0542%20(Ai-Chang)%20v3.pdf>. •

Chien, Steve and Morris, Robert. “Space Applications of Artificial Intelligence”. AI Magazine. [online]. 2014. [22 December 2020]. Available from: <Space Applications of Artificial Intelligence - Association for ...www.aaai.org › index.php › aimagazine › article › view>.

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Das, Puja. “Intel Welcomes First Satellite Made Out of Artificial Intelligence”. Analytics Insight. [online]. 21 October 2020. [22 December 2020]. Available from: <https://www.analyticsinsight.net/intel-welcomes-first-satellite-made-artificialintelligence/>.

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Das, Sejuti. “How Artificial Intelligence is Helping in Space Exploration”. Analytics India Magazine. [online]. 21 January 2020. [21 December 2020]. Available from: <https://analyticsindiamag.com/is-artificial-intelligence-the-new-guardian-of-thegalaxy/>.

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D’ Monte, Leslie. “Chandrayaan- 2 Pragyan Shows How AI is Helping Space Exploration”. Livemint. [online]. 06 September 2019. [23 December 2020]. Available from: <https://www.livemint.com/technology/tech-news/chandrayaan-2-pragyan-shows-howai-is-helping-space-exploration-1567764065716.html>.

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Eduonix. “Artificial Intelligence in Space Exploration”. Eduonix. [online]. 08 March 2019. [23 December 2020]. Available from: <https://blog.eduonix.com/artificialintelligence/artificial-intelligence-space-exploration/>.

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European Space Agency. “ENVISAT”. ESA. [online]. 2019. [22 December 2020]. Available from: <https://earth.esa.int/eogateway/missions/envisat>.

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France- Presse, Agence. “AI Robot ‘Flying Brain’ Blasts Off Towards ISS Aboard SpaceX Spacecraft”. Tech2. [online]. 02 July 2018. [22 December 2020]. Available from: <https://www.firstpost.com/tech/science/ai-robot-flying-brain-blasts-off-towards-issaboard-spacex-spacecraft-4643821.html>.

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Giuliano, M. “Artificial Intelligence Technologies Used in the SPIKE Planning and Scheduling System”. Space Telescope Science Institute. [online]. 2018. [22 December 2020]. Available from: <https://www.stsci.edu/contents/newsletters/2018-volume-35-issue03/artificial-intelligence-techniques-used-in-the-spike-planning-and-scheduling-system>.

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Hurley, Billy. “A Faster Way to Design Rockets: Scientific Machine Learning”. Tech Briefs. [online]. 23 April 2020. [22 December 2020]. Available from: <https://www.techbriefs.com/component/content/article/tb/stories/blog/36755>.

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Ingham, Lucy. “AI Harnessed to Tackle Space Debris With GBP 1M From UK Space Agency”. Verdict. [online]. 16 September 2020. [23 December 2020]. Available from: <https://www.verdict.co.uk/ai-space-debris-uk/>.

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Japan Aerospace Exploration Agency. “A New Type of Launch Vehicle: A Rocket With Artificial Intelligence”. JAXA. [online]. 2003. [22 December 2020]. Available from: <https://global.jaxa.jp/article/interview/vol58/index_e.html>.

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Jewett, Rachel and Holmes, Mark. “10 Ways AI is Making a Difference in the Satellite Industry”. Via Satellite. [online]. October 2020. [22 December 2020]. Available from: <http://interactive.satellitetoday.com/via/october-2020/10-ways-ai-is-making-adifference-in-the-satellite-industry/>.

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2020 Handbook on Artificial Intelligence and International Law Johnston, Mark and Miller, Glenn. “SPIKE: Intelligent Scheduling of Hubble Space Telescope Observations”. Space Telescope Science Institute. [online]. 1992. [22 December 2020]. Available from: <https://www.stsci.edu/~miller/papers-and-meetings/93-IntelligentScheduling/spike/spike-chapter3.html>.

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Macaulay, Thomas. “AI to Help World’s First Removal of Space Debris”. The Next Web. [online]. 30 October 2020. [23 December 2020]. Available from: <https://thenextweb.com/neural/2020/10/30/ai-to-help-worlds-first-removal-of-spacedebris/>.

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Scharf, Michael. “Outer Space Law”. Customary International Law in Times of Fundamental Change. (June 2013). Cambridge University Press. pp. 123- 138.

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The 1963 Treaty Banning Nuclear Weapon Tests in the Atmosphere, in Outer Space and Under Water (Partial Nuclear Test Ban Treaty), Treaty No. 6964.

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The 1967 Treaty on Principles Governing the Activities of States in the Exploration and Use of Outer Space, Including the Moon and Other Celestial Bodies, General Assembly Resolution 2222 (XI).

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The 1972 Convention on International Liability for Damage Caused by Space Objects, General Assembly Resolution 2777 (XXVI).

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The 1976 Convention on Registration of Objects Launched Into Outer Space, General Assembly Resolution 3235 XXIX.

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The 1982 Principles Governing the Use by States of Artificial Earth Satellites for International Direct Television Broadcasting, General Assembly Resolution 37/92.

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Turrini, Paolo. “The Sky’s Not the Limit: Legal Bonds and Boundaries in Claiming Sovereignty Over Celestial Bodies”. Borders, Legal Spaces and Territories in Contemporary International Law. pp. 173- 209. [online]. September 2019. [21 December 2020]. Available from: <https://www.researchgate.net/publication/335784567_The_Sky%27s_Not_the_Limit_ Legal_Bonds_and_Boundaries_in_Claiming_Sovereignty_over_Celestial_Bodies>.

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Airport AI. “Customer Service Platform For Airports”. Airport AI. [online]. 2020. [28 December 2020]. Available from: <https://www.airport.ai/#technology>. Altexsoft. “7 Ways How Airline Industry Uses Artificial Intelligence in Operations”. Altexsoft. [online]. 10 July 2018. [27 December 2020]. Available from: <https://www.altexsoft.com/blog/datascience/7-ways-how-airlines-use-artificialintelligence-and-data-science-to-improve-their-operations/>. American Historical Association. “What are the ‘Five Freedoms’ of Air Transport”. American Historical Association. [online]. 2020. [27 December 2020]. Available from: <https://www.historians.org/about-aha-and-membership/aha-history-and-archives/giroundtable-series/pamphlets/em-17-how-free-are-the-skyways-(1945)/what-are-the-fivefreedoms-of-air-transport>. AMFG. “AI & Aerospace: 5 Ways Artificial Intelligence Could Impact Aviation”. AMFG. [online]. 31 August 2018. [29 December 2020]. Available from: <https://amfg.ai/2018/08/31/ai-aerospace-5-ways-artificial-intelligence-could-impactaviation/>. Asquith, James. “If Aviation Was A Country It Would Be The World’s 20th Largest By GDP”. Forbes. [online]. 06 April 2020. [25 December 2020]. Available from: <https://www.forbes.com/sites/jamesasquith/2020/04/06/if-aviation-was-a-country-itwould-be-the-worlds-20th-largest-by-gdp/?sh=2bc63972e5b5>. Aviation Business News. “How Artificial Intelligence is Supporting the Aviation Industry”. Aviation Business News. [online]. April 2018. [28 December 2020]. Available from: <https://www.aviationbusinessnews.com/low-cost/artificial-intelligence-aviationindustry/>. BAA Training. “Artificial Intelligence Putting Roots in Aviation”. BAA Training. [online]. 29 October 2018. [28 December 2020]. Available from: <https://www.baatraining.com/artificial-intelligence-putting-roots-in-aviation/>. Balaganur, Sameer. “How the Aviation Industry is Joining Hands With AI to Improve Air Safety”. Analytics India Magazine. [online]. 29 January 2020. [27 December 2020]. Available from: <https://analyticsindiamag.com/how-the-aviation-industry-joining-hand-with-aito-improve-air-safety/>. Baranick, Marsha. “Artificial Intelligence Can Improve Pilot Training, Mission Success”. Rotor & Wing International. [online]. 11 September 2018. [28 December 2020]. Available from: <https://www.rotorandwing.com/2018/09/11/artificial-intelligence-can-improvepilot-training-mission-success/>. Bernard, Marr. “The Amazing Ways Dubai Airport Uses Artificial Intelligence”. Forbes. [online]. 26 July 2019. [28 December 2020]. Available from: <https://www.forbes.com/sites/bernardmarr/2019/07/26/the-amazing-ways-dubaiairport-uses-artificial-intelligence/?sh=42a050152a21>. Blum, Selma. “Behaviour Detection Technology: Screening on the Go”. Aviation Security International Magazine. [online]. 18 April 2020. [28 December 2020]. Available from: <https://www.asi-mag.com/behaviour-detection-technology-screening-on-the-go/>. Cambridge Dictionary. “Civil Aviation”. Cambridge English Dictionary. [online]. 2020. [25 December 2020]. Available from: <https://dictionary.cambridge.org/dictionary/english/civil-aviation>. Centre for International Law. “2010 Convention on the Suppression of Unlawful Acts Relating to International Civil Aviation”. National University of Singapore. [online]. 2010. [27 December 2020]. Available from: <https://cil.nus.edu.sg/wpcontent/uploads/2017/08/2010-Convention-on-the-Suppression-of-Unlawful-ActsRelating-to-International-Civil-Aviation-1.pdf>.

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Bandoim, Lana. “How Self- Driving Tractors and AI are Changing Agriculture”. Forbes. [online]. 27 April 2019. [30 December 2020]. Available from: <https://www.forbes.com/sites/lanabandoim/2019/04/27/how-self-driving-tractorsand-ai-are-changing-agriculture/?sh=2301310c7fa1>.

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Baraniuk, Chris. “The New Weapon in the Fight Against Crime”. BBC Future. [online]. 04th March 2019. [31 December 2020]. Available from: <https://www.bbc.com/future/article/20190228-how-ai-is-helping-to-fight-crime>.

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Cutieru, Andreea. “Pioneers: 6 Practices Bringing AI into Architecture”. ArchDaily. [online]. 07 April 2020. [31 December 2020]. Available from: <https://www.archdaily.com/936999/pioneers-6-practices-bringing-ai-intoarchitecture>.

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