Rage Inside the Machine by Robert Elliott Smith

RAGE INSIDE THE MACHINE

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RAGE INSIDE THE MACHINE The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All

ROBERT ELLIOTT SMITH

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BLOOMSBURY BUSINESS Bloomsbury Publishing Plc 50 Bedford Square, London, WC1B 3DP, UK 1385 Broadway, New York, NY 10018, USA BLOOMSBURY, BLOOMSBURY BUSINESS and the Diana logo are trademarks of Bloomsbury Publishing Plc First published in Great Britain 2019 Copyright Š Robert Elliott Smith, 2019 Cover design by Alice Marwick Robert Elliott Smith has asserted his right under the Copyright, Designs and Patents Act, 1988, to be identified as Author of this work.

All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage or retrieval system, without prior permission in writing from the publishers.

Bloomsbury Publishing Plc does not have any control over, or responsibility for, any third-­party websites referred to or in this book. All internet addresses given in this book were correct at the time of going to press. The author and publisher regret any inconvenience caused if addresses have changed or sites have ceased to exist, but can accept no responsibility for any such changes.

A catalogue record for this book is available from the British Library.

A catalog record for this book is available from the Library of Congress.

ISBN: HB: 978-1-4729-6388-8

ePDF 978-1-4729-6390-1

eBook: 978-1-4729-6389-5 Typeset by RefineCatch Limited, Bungay, Suffolk Printed and bound in Great Britain To find out more about our authors and books visit www.bloomsbury.com and sign up for our newsletters.

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CONTENTS

Preface ix

1 Mechanisms of Morality 2 Doctor Illuminatus

1

17

3 The Death of Uncertainty

37

4 Scientific Pride and Prejudice 5 AIQ

63

87

6 Value Instead of Values 7 Women’s Work

119

151

8 What Is Mind, No Matter 9 Defining Terms

175

207

10 It’s a Lot More Complicated than We Think 11 Strength in Diversity 12 Gods and Monsters

239

269 293

Notes 311 Index 325

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To my tireless reader, insightful editor, constant inspiration, and wonderful wife, Paula Hardy. She made this book possible.

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PREFACE

In recent years we’ve all been hearing about earth-­shattering advances in the abilities of algorithms. The artificial intelligence (AI) in these computer programs is said to be advancing so rapidly that it will soon match or exceed human capabilities. In 2014, the term ‘superintelligence’1 emerged to describe AI, suggesting the apparently inevitable superiority of algorithms over human intelligence. And, at the 2016 World Economic Forum at Davos, the focus was on the predicted elimination of most human jobs by machines, which we’re told will soon be able to do those jobs just as well as, if not better than, people.

Some of the greatest minds of our time, including Stephen Hawking, Henry Kissinger,2 Elon Musk3 and Bill Gates,4 have expressed concerns about the future dominance of intelligent machines over people. Stephen Hawking went as far as to say: ‘I think the development of full artificial intelligence could spell the end of the human race.’5 The path to a world dominated by machines and machine intelligence now seems inevitable, and we are told to feel confident in these predictions, in part because they themselves are the results of algorithms that have analysed real-­ world ‘big data’, thus avoiding human subjectivity. A belief in algorithmic objectivity is not at all unusual today. Furthermore, there is a widely held tendency to assume that digital technology is inevitably meritocratic, democratic and libertarian, and that any regulation would have a negative effect on its optimal, self-­organizing systems.

So-­called ‘smart’ AI is now being used to understand such big data and draw conclusions from that data that profoundly influence our lives in the real world. Algorithms do everything from assigning people hour-­to-hour work (Deliveroo, Uber, etc.), to selecting the news we read (Google, Facebook,

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x

Preface

Twitter, etc.), recommending the products we might buy (Amazon, Google, etc.), the friends we might like (Facebook) and the partners we might marry (Match.com, eHarmony, etc.). Forbes6 recently revealed that in China, 80 percent of surveyed citizens approve of a government-­sponsored big data algorithm that will soon reduce all of their individual financial, civic, and social media activity (along with inputs from CCTV systems that can recognize their faces) to a single score, which will determine their access to travel, housing, healthcare, and high-­quality goods.

Even in the Western world, we largely accept (with some enthusiasm) that online algorithms process data about us and shape most of our interactions, yet we’re largely unaware of exactly how, mostly don’t understand their operation, and barely grasp the influence they exert on our lives. Our willing, but uninformed, consent to their operation in our lives implicitly assumes that these AI programs are benign and unbiased, because they can only perform rational computations.

We believe that the results served up to us in online lists and searches are a true reflection of the world and the choices available to us therein. Numbers don’t lie, and since machines just process numbers, neither can they.

However, in the last few years, algorithms have been generating some surprisingly unsavoury and unexpected outputs. In 2015, the Guardian reported that Google algorithms tagged images of black people as ‘#animals’, ‘#apes’ and ‘#gοrillas’.7 They also reported that Google image searches for ‘unprofessional hair’ predominately returned pictures of black women.8 Another report revealed that Google’s algorithms showed high-­paying job ads to men more often than to women.9 Then, the Observer revealed that Google’s auto-­suggest algorithm completed the searches ‘are women . . .’ and ‘are Jews . . .’ with the word ‘evil’, and that clicking on these suggestions returned pages of affirmative answers to those questions.10 Similarly, when Microsoft released a Twitter bot (AI algorithm) called ‘Tay’ in 2016, it had to be shut down rapidly after just 24 hours of operation, because it had learned to say, ‘I fucking hate feminists and they should all die’, ‘Hitler was right I hate the

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Preface

xi

Jews’ and ‘WE’RE GOING TO BUILD A WALL, AND MEXICO IS GOING TO PAY FOR IT’.11 You may well ask yourself, ‘What on earth is going on?’ Have racist and misogynistic computer programmers run amok in their Silicon Valley offices and created algorithmic monsters imbued with humanity’s worst characteristics? Do these unbiased, objective algorithmic results simply reveal the ugly truth about human society hidden in our big data? Or is there something else entirely going on? And is that something else something specific about the algorithms themselves? If so, what is it, and how is it affecting us and society at large? And how can it be stopped and changed for the better?

The first step in the process is a better understanding of what algorithms are, how they operate and how they have evolved since people started imagining machines that could calculate and maybe even, one day, think. We aren’t familiar with this story of algorithmic evolution because, unlike the Arts and Humanities, scientific subjects are taught in the abstract, entirely devoid of historical, cultural and social context. When we learn about Shakespeare, we see him placed in the cultural context of Elizabethan England right down to his pantaloons. This offers us a greater insight into his plays, subject matter and characters, because we understand their context. Likewise, the teachings of toga-­wearing Aristotle are placed firmly in the context of Classical Greece, and Da Vinci can’t be disconnected from the cultural dynamism of the Renaissance.

Literature, art, philosophy, music and so on are all taught in parallel with one another and their historical context.

In contrast, the maths and science at the heart of algorithms is taught disconnected from any context, as if the theories and inventions in these areas are entirely abstract unassailable truths, beyond the influences of the historical periods in which they arose. But if we are to examine algorithms for the possible biases they might carry, we have to acknowledge that there are assumptions deep within these procedures that are influenced by the times and places in which they were created. This book steps through those times

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and places to offer a view on the historical and cultural connections that have shaped the creation of algorithms. The connections are numerous, from the representation of thinking as the ‘if–then’ rules of Aristotelian logic, to the modelling of uncertainty based on calculations drawn from dice games, through the reduction of evolution to a philosophy of ‘survival of the fittest’, to the attempt to capture complex human characteristics like ‘intelligence’ in numbers like IQ, to the transformation and reduction of human artisanship into discrete tasks neatly divided to fit mass-­manufacturing processes, through an unassailable faith that free markets generate spontaneous order, to viewing the living brain as a simple, synaptic computer, to a staggering reduction of the subtle meanings of language to the bit transmissions of information theory.

Each of these simplifications arose in a particular historical and cultural context and has a direct connection to the conception and design of algorithms that now operate all around us today.

Just as algorithms have emerged from various historical and cultural contexts, they are also a product engineered by people. At some point, a human being touches almost every part of an algorithm, whether it’s finding the dataset the algorithm will work upon, manually tagging and classifying some of that data, deciding the parameters that control the algorithm, determining its goals and evaluating its performance. I am one of those people, an engineer by training, an experienced AI consultant and a member of the computer science faculty at University College, London (UCL).

At large technology corporations like Google and Facebook, there are whole communities of scientists like me interacting and working on the creation of algorithms that now frame and act upon our lives. They themselves are the product of different cultures and ideas, specific even to the groups they may work in. Similarly, the impacts of their design decisions are social, and while the cold computations of algorithms treat society as a body of statistics, it is actually a body of individuals immersed in richly varied cultural and social contexts to which we need to be sensitive. To acknowledge the deep connection

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xiii

between algorithms and ourselves at every level, this book includes personal stories of my own life and career in AI to ground its observations in at least one person’s real-­world experiences.* Because the only way to really understand the impact of algorithms is to understand them in relationship to the individuals and society to which they are bound.

* Please note that names of people in the personal stories have been changed, with the exception of any professional colleagues who are cited in the references.

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