Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
Misapplication of Existing Ethical Tools Privacy and Informed Consent
Problems/Recurring
Mistakes
Technical Insensitivity Autonomous Vehicles (Normative) Ethical Insensitivity Algorithmic Opacity
Lack of Stakeholder Engagement Contact Tracing
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
Traditional Biomedical Research
Foundational Value Respect for Persons
Intermediary Norm Promote and Protect Autonomy Practical Tool Informed Consent and Anonymization
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
Big Data Analytics
Foundational Value Respect for Persons
Intermediary Norm Promote and Protect Autonomy
Practical Tool Notice & Consent and Anonymization
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
Optimists
Responses to the AV Trolley Problem Comparison
Survey-Based Expert-Based
Pessimimists Too focused on edge-cases Thought experiments are unrealistic Trolley Cases are disanalogous
Introduction Pain Points
Ethical Insensitivity Ethics Ecosystems Conclusion
Algorithms
Traditional Machine Learning
Introduction
Ethical Insensitivity Ethics Ecosystems Conclusion
Algorithms
Ethical Insensitivity Ethics Ecosystems Conclusion
Training
Behavior in Particular Scenarios
Ethical Insensitivity Ethics Ecosystems Conclusion
n. Behavior: Should AVs be programmed to behave in accident scenarios in ways that conform with the appropriate verdict in similarly structured Trolley Cases?
1. Regime: How much of the training regime should be dedicated to dealing explicitly with accident scenarios?
2. Structure: Should accident scenarios in the training set be structured like Trolley Cases (and which ones)? 3. ……. n. Behavior: Should AVs be programmed to behave in accident scenarios in ways that conform with the appropriate verdict in similarly structured Trolley Cases?
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
Ethical Insensitivity Ethics Ecosystems Conclusion
Interpretability Thesis
In many contexts, decision-makers have a (defeasible) moral obligation to avoid basing their decisions about how to treat decision-subjects on outputs of noninterpretable (“black box”) algorithmic decision systems.
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
The Interpretability
Thesis
The Transparency Defense
Defend Duties of Transparency Defend Interference
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems
Conclusion
Definition Problem
The Interpretability
Thesis The Transparency Defense
Defend Duties of Transparency Grounding Problem Defend Interference DoubleStandards Problem
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
The Interpretability Thesis
The Due Consideration Defense
Defend Duties of Consideration Defend Interference
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
The Due
Considerations Defense of the Interpretability Thesis
• We have duties to employ an appropriate decisionlogic when making decisions about decisionsubjects.
• Black box systems impede decision-makers abilities to show due consideration to decision-subjects.
Ethical Insensitivity Ethics Ecosystems Conclusion
Duties of Consideration
Duties of Evidential Consideration
Duties of Practical Consideration
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
Duties of Evidential Consideration
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
Duties of Ignorance
• Decision-makers have duties to ignore certain evidence.
• E.g., morally inadmissible evidence
Morally Inadmissible Evidence
• A piece of evidence is morally inadmissible with respect to a decision-maker and a decision when that decision-maker is obligated to set aside that piece of evidence when making that decision.
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
Ethical Insensitivity Ethics Ecosystems Conclusion
Duties of Consideration
Duties of Evidential Consideration
Duties of Practical Consideration
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion The Interpretability Thesis and Ethical Sensitivity
• The Due Consideration Defense doesn’t simply ground the Interpretability Thesis.
• It highlights the normative dimensions of what it means to “open the black box”.
Points
Ethical Insensitivity Ethics Ecosystems Conclusion
Lessons from pain points:
• Understand the technology
• Technical solutions can’t be normatively free floating
• Keep context in mind
• Think socio-technical system instead of simply technical system
• Engage key stakeholders
Ethical Insensitivity Ethics Ecosystems Conclusion
Lessons from pain points:
• Understand the technology
• Technical solutions can’t be normatively free floating
• Keep context in mind
• Think socio-technical system instead of simply technical system
• Engage key stakeholders
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
Outreach, Education, Enculturation
Foundational to Translational Research Programs
Common Language and Interdisciplinary Scholars
Ethics Infrastructure
Interdisciplinary Practice
Informed Policy and Regulation
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
Healthcare (HSR, ASR, Clinical Care)
AI and Big Data
Ethics Infrastructure IRB; IACUC; Standard Protocols NIST AI RMF
Policy and Regulation The Common Rule; AWA Biden EO and AISI; State and City statutes on ADS
Outreach, Training, Education, and Enculturation
Required trainings; Curricular Requirements; Disclosure Requirements Embedded Ethics Curricula; AIDE Summer
Interdisciplinary Practice Standard
No… Research Programs (and Exchange) Bioethics; Case Study Repositories; IRB Conferences…
AI & Ethics, Phil & Tech; FAaccT, AIES; Hiring trends
Shared Language and Interdisciplinary Scholars
“Informed Consent”, “Exempt Research”; Bioethicists “Fairness”, “Transparency”, “Privacy”,….; AI Ethicists?
How?
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems Conclusion Needs: Foundational to Translational Research
• Sample Areas:
• Fairness and Bias
• Performance and Evaluation
• Transparency and Explanation
• Privacy
• Political Philosophy of AI
• Emerging Techniques
Introduction Pain Points Technical Insensitivity
Introduction Pain Points Technical Insensitivity
Introduction Pain Points Technical Insensitivity
Ethical Insensitivity Ethics Ecosystems
Lessons and Opportunities:
• Orient work towards building an ecosystem.
• Experiment to fill the gap between now and a more robust ecosystem.
• Humanists needed!
Outreach, Education, Enculturation
Foundational to Translational Research Programs
Common Language and Interdisciplinary Scholars
Interdisciplinary Practice
Informed Policy and Regulation Ethics Infrastructure
