67 Daniel Miller, Evan Brossard, Steven M. Seitz and Ira Kemelmacher-Shlizerman, The MegaFace Benchmark: 1 Million Faces for Recognition at Scale, 2015, https://arxiv.org/pdf/1505.02108.pdf.
68 Ibid.
69 MegaFace and MF2: Million-Scale Face Recognition, ‘Most recent public results’, 12 March 2017, http://megaface.cs.washington.edu/; ‘Leading facial recognition platform Tencent YouTu Lab smashes records in MegaFace facial recognition challenge’, Cision PR Newswire, 14 April 2017, http://www.prnewswire.com/news-releases/leading-facial-recognition-platform-tencent-youtu-lab-smashes-records-in-megaface-facial-recognition-challenge-300439812.html.
70 Dan Robson, ‘Facial recognition a system problem gamblers can’t beat?’, TheStar.com, 12 Jan. 2011, https://www.thestar.com/news/gta/2011/01/12/facial_recognition_a_system_problem_gamblers_cant_beat.html.
71 British Retail Consortium, 2016 Retail Crime Survey (London: BRC, Feb. 2017), https://brc.org.uk/media/116348/10081-brc-retail-crime-survey-2016_all-graphics-latest.pdf.
72 D&D Daily, The D&D Daily’s 2016 Retail Violent Death Report, 9 March 2017, http://www.d-ddaily.com/archivesdaily/DailySpecialReport03-09-17F.htm.
73 Joan Gurney, ‘Walmart’s use of facial recognition tech to spot shoplifters raises privacy concerns’, iQ Metrix, 9 Nov. 2015, http://www.iqmetrix.com/blog/walmarts-use-of-facial-recognition-tech-to-spot-shoplifters-raises-privacy-concerns.
74 Andy Coghlan and James Randerson, ‘How far should fingerprints be trusted?’, New Scientist, 14 Sept. 2005, https://www.newscientist.com/article/dn8011-how-far-should-fingerprints-be-trusted/.
75 Phil Locke, ‘Blood spatter – evidence?’, The Wrongful Convictions Blog, 30 April 2012, https://wrongfulconvictionsblog.org/2012/04/30/blood-spatter-evidence/.
76 Michael Shermer, ‘Can we trust crime forensics?’, Scientific American, 1 Sept. 2015, https://www.scientificamerican.com/article/can-we-trust-crime-forensics/.
77 National Research Council of the National Academy of Sciences, Strengthening Forensic Science in the United States: A Path Forward (Washington DC: National Academies Press, 2009), p. 7, https://www.ncjrs.gov/pdffiles1/nij/grants/228091.pdf.
78 Colin Moynihan, ‘Hammer attacker sentenced to 22 years in prison’, New York Times, 19 July 2017, https://www.nytimes.com/2017/07/19/nyregion/hammer-attacker-sentenced-to-22-years-in-prison.html?mcubz=0.
79 Jeremy Tanner, ‘David Baril charged in hammer attacks after police-involved shooting’, Pix11, 14 May 2015, http://pix11.com/2015/05/14/david-baril-charged-in-hammer-attacks-after-police-involved-shooting/.
80 ‘Long-time fugitive captured juggler was on the run for 14 years’, FBI, 12 Aug. 2014, https://www.fbi.gov/news/stories/long-time-fugitive-neil-stammer-captured.
81 Pei-Sze Cheng, ‘I-Team: use of facial recognition technology expands as some question whether rules are keeping up’, NBC 4NewYork, 23 June 2015, http://www.nbcnewyork.com/news/local/Facial-Recognition-NYPD-Technology-Video-Camera-Police-Arrest-Surveillance-309359581.html.
82 Nate Berg, ‘Predicting crime, LAPD-style’, Guardian, 25 June 2014, https://www.theguardian.com/cities/2014/jun/25/predicting-crime-lapd-los-angeles-police-data-analysis-algorithm-minority-report.
Art
1 Matthew J. Salganik, Peter Sheridan Dodds and Duncan J. Watts, ‘Experimental study of inequality and unpredictability in an artificial cultural market’, Science, vol. 311, 10 Feb. 2006, p. 854, DOI: 10.1126/science.1121066, https://www.princeton.edu/~mjs3/salganik_dodds_watts06_full.pdf.
2 http://www.princeton.edu/~mjs3/musiclab.shtml.
3 Kurt Kleiner, ‘Your taste in music is shaped by the crowd’, New Scientist, 9 Feb. 2006, https://www.newscientist.com/article/dn8702-your-taste-in-music-is-shaped-by-the-crowd/.
4 Bjorn Carey, ‘The science of hit songs’, LiveScience, 9 Feb. 2006, https://www.livescience.com/7016-science-hit-songs.html.
5 ‘Vanilla, indeed’, True Music Facts Wednesday Blogspot, 23 July 2014, http://truemusicfactswednesday.blogspot.co.uk/2014/07/tmfw-46-vanilla-indeed.html.
6 Matthew J. Salganik and Duncan J. Watts, ‘Leading the herd astray: an experimental study of self-fulfilling prophecies in an artificial cultural market’, Social Psychology Quarterly, vol. 74, no. 4, Fall 2008, p. 338, DOI: https://doi.org/10.1177/019027250807100404.
7 S. Sinha and S. Raghavendra, ‘Hollywood blockbusters and long-tailed distributions: an empirical study of the popularity of movies’, European Physical Journal B, vol. 42, 2004, pp. 293–6, DOI: https://doi.org/10.1140/epjb/e2004-00382-7; http://econwpa.repec.org/eps/io/papers/0406/0406008.pdf.
8 ‘John Carter: analysis of a so-called flop: a look at the box office and critical reaction to Disney’s early tentpole release John Carter’, WhatCulture, http://whatculture.com/film/john-carter-analysis-of-a-so-called-flop.
9 J. Valenti, ‘Motion pictures and their impact on society in the year 2000’, speech given at the Midwest Research Institute, Kansas City, 25 April 1978, p. 7.
10 William Goldman, Adventures in the Screen Trade (New York: Warner, 1983).
11 Sameet Sreenivasan, ‘Quantitative analysis of the evolution of novelty in cinema through crowdsourced keywords’, Scientific Reports 3, article no. 2758, 2013, updated 29 Jan. 2014, DOI: https://doi.org/10.1038/srep02758, https://www.nature.com/articles/srep02758.
12 Márton Mestyán, Taha Yasseri and János Kertész, ‘Early prediction of movie box office success based on Wikipedia activity big data’, PLoS ONE, 21 Aug. 2013, DOI: https://doi.org/10.1371/journal.pone.0071226.
13 Ramesh Sharda and Dursun Delen, ‘Predicting box-office success of motion pictures with neural networks’, Expert Systems with Applications, vol. 30, no. 2, 2006, pp. 243–4, DOI: https://doi.org/10.1016/j.eswa.2005.07.018; https://www.sciencedirect.com/science/article/pii/S0957417405001399.
14 Banksy NY, ‘Banksy sells work for $60 in Central Park, New York – video’, Guardian, 14 Oct. 2013, https://www.theguardian.com/artanddesign/video/2013/oct/14/banksy-central-park-new-york-video.
15 Bonhams, ‘Lot 12 Banksy: Kids on Guns’, 2 July 2014, http://www.bonhams.com/auctions/21829/lot/12/.
16 Charlie Brooker, ‘Supposing … subversive genius Banksy is actually rubbish’, Guardian, 22 Sept. 2006, https://www.theguardian.com/commentisfree/2006/sep/22/arts.visualarts.
17 Gene Weingarten, ‘Pearls before breakfast: can one of the nation’s greatest musicians cut through the fog of a DC rush hour? Let’s find out’, Washington Post, 8 April 2007, https://www.washingtonpost.com/lifestyle/magazine/pearls-before-breakfast-can-one-of-the-nations-great-musicians-cut-through-the-fog-of-a-dc-rush-hour-lets-find-out/2014/09/23/8a6d46da-4331-11e4-b47c-f5889e061e5f_story.html?utm_term=.a8c9b9922208.
18 Quotations from Armand Leroi are from personal communication. The study he refers to is: Matthias Mauch, Robert M. MacCallum, Mark Levy and Armand M. Leroi, ‘The evolution of popular music: USA 1960–2010’, Royal Society Open Science, 6 May 2015, DOI: https://doi.org/10.1098/rsos.150081.
19 Quotations from David Cope are from personal communication.
20 This quote has been trimmed for brevity. See Douglas Hofstadter, Gödel, Escher, Bach: An Eternal Golden Braid (London: Penguin, 1979), p. 673.
21 George Johnson, ‘Undiscovered Bach? No, a computer wrote it’, New York Times, 11 Nov. 1997.
22 Benjamin Griffin and Harriet Elinor Smith, eds, Autobiography of Mark Twain, vol. 3 (Oakland, CA, and London, 2015), part 1, p. 103.
23 Leo Tolstoy, What Is Art? (London: Penguin, 1995; first publ. 1897).
24 Hofstadter, Gödel, Escher, Bach, p. 674.
Conclusion
1 For Rahinah Ibrahim’s story, see https://www.propublica.org/article/fbi-checked-wrong-box-rahinah-ibrahim-terrorism-watch-list; https://alumni.stanford.edu/get/page/magazine/article/?article_id=66231.
2 GenPact, Don’t underestimate importance of process in coming world of AI, 14 Feb. 2018, http://www.genpact.com/insight/blog/dont-underestimate-importance-of-process-in-coming-world-of-ai.
Ackn
owledgements
THERE ARE SOME people, I imagine, who find writing easy. You know the sort – the ones who jump out of bed before sunrise, have a chapter written by lunch and forget to come down to dinner because they’re so at one with their creative flow that they just didn’t realize the time.
I am definitely not one of those people.
Getting through this process involved a daily battle with the side of my character that just wants to sit on the sofa eating crisps and watching Netflix, and an all-out war with the tsunamis of worry and panic that I thought I’d left behind when I finished my PhD. I didn’t really write this book so much as drag it out of myself, kicking and screaming. Sometimes literally.
So I’m all the more grateful to the remarkable group of people who were willing to help me along the way. My fantastic publishing team, who have been so generous with their time and ideas over the past year: Susanna Wadeson, Quynh Do, Claire Conrad, Emma Parry, Gillian Somerscales, Emma Burton, Sophie Christopher, Hannah Bright, Caroline Saine and all the people at Janklow and Nesbit, Transworld and Norton who have been helping behind the scenes. Likewise, Sue Rider, Kat Bee and Tom Copson. I’d be lost without you.
Enormous thanks, too, to my interviewees, some of whom are quoted in the text, but all of whom helped shape the ideas for the book: Jonathan Rowson, Nigel Harvey, Adam Benforado, Giles Newell, Richard Berk, Sheena Urwin, Steyve Colgan, Mandeep Dhami, Adrian Weller, Toby Davies, Rob Jenkins, Jon Kanevsky, Timandra Harkness, Dan Popple and the team at West Midlands police, Andy Beck, Jack Stilgoe, Caroline Rance, Paul Newman, Phyllis Illarmi, Armand Leoni, David Cope, Ed Finn, Kate Devlin, Shelia Hayman, Tom Chatwin, Carl Gombrich, Johnny Ryan, Jon Crowcroft and Frank Kelly.
There’s also Sue Webb and Debbie Enright from Network Typing and Sharon Richardson, Shruthi Rao and Will Storr, whose help in wrestling this book into shape was invaluable. Plus, once I had finally something approaching sentences written down, James Fulker, Elisabeth Adlington, Brendan Maginnis, Ian Hunter, Omar Miranda, Adam Dennett, Michael Veale, Jocelyn Bailey, Cat Black, Tracy Fry, Adam Rutherford and Thomas Oléron Evans, all helped me find the biggest flaws and beat them into submission. And Geoff Dahl, who, as well as offering moral support throughout this entire process, also had the very clever idea for the cover design.
Very many thanks to my peer reviewers: Elizabeth Cleverdon, Bethany Davies, Ben Dickson, Mike Downes, Charlie and Laura Galan, Katie Heath, Mia Kazi-Fornari, Fatah Ioualitene, Siobhan Mathers, Mabel Smaller, Ali Seyhun Saral, Jennifer Shelley, Edward Steele, Daniel Vesma, Jass Ubhi.
I am also unimaginably grateful to my family, for their unwavering support and steadfast loyalty. Phil, Tracy, Natalie, Marge & Parge, Omar, Mike and Tania – you were more patient with me than I often deserved. (Although don’t take that too literally, because I probably am going to write another book, and I need you to help me again, OK?)
And last, but by no means least, Edith. Frankly, you were no help whatsoever, but I wouldn’t have had it any other way.
Index
The page references in this index correspond to the printed edition from which this ebook was created. To find a specific word or phrase from the index, please use the search feature of your ebook reader.
Endnotes in the index are denoted by an ‘n’ after the page number e.g. ‘ambiguous images 211n13’
23andMe 108–9
profit 109
promises of anonymity 109
sale of data 109
volume of customers 110
abnormalities 84, 87, 95
acute kidney injuries 104
Acxiom 31
Adele 193
advertising 33
online adverts 33–5
exploitative potential 35
inferences 35
personality traits and 40–1
political 39–43
targeted 41
AF447 (flight) 131–3, 137
Afigbo, Chukwuemeka 2
AI (artificial intelligence) 16–19
algorithms 58, 86
omnipotence 13
threat of 12 see also DeepMind
AI Music 192
Air France 131–3
Airbnb, random forests 59
Airbus A330 132–3
algebra 8
algorithmic art 194
algorithmic regulating body 70
algorithms aversion 23
Alhambra 156
Alton Towers 20–1
ALVINN (Autonomous Land Vehicle In a Neural Network) 118–19
Alzheimer’s disease 90–1, 92
Amazon 178
recommendation engine 9
ambiguous images 211n13
American Civil Liberties Union (ACLU) 17
Ancestry.com 110
anchoring effect 73
Anthropometric Laboratory 107–8
antibiotics 111
AOL accounts 2
Apple 47
Face ID system 165–6
arithmetic 8
art 175–95
algorithms 184, 188–9
similarity 187
books 178
films 180–4
popularity 183–4
judging the aesthetic value of 184
machines and 194
meaning of 194
measuring beauty 184–5
music 176–80
piano experiment 188–90
popularity 177, 178, 179
quality 179, 180
quantifying 184–8
social proof 177–8, 179
artifacts, power of 1–2
artificial intelligence (AI) see AI (artificial intelligence)
association algorithms 9
asthma 101–2
identifying warning signs 102
preventable deaths 102
Audi
slow-moving traffic 136
traffic jam pilot 136
authority of algorithms 16, 198, 199, 201
misuse of 200
automation
aircraft 131–3
hidden dangers 133–4
ironies of 133–7
reduction in human ability 134, 137 see also driverless cars
Autonomous Emergency Braking system 139
autonomy 129, 130
full 127, 130, 134, 138
autopilot systems
A330 132
driverless cars 134
pilot training 134
sloppy 137
Tesla 134, 135, 138
bail
comparing algorithms to human judges 59–61
contrasting predictions 60
success of algorithms 60–1
high-risk scores 70
Bainbridge, Lisanne 133–4, 135, 138
balance 112
Banksy 147, 185
Baril, David 171–2
Barstow 113
Bartlett, Jamie 44
Barwell, Clive 145–7
Bayes’ theorem 121–4, 225n30
driverless cars 124
red ball experiment 123–4
simultaneous hypotheses 122–3
Bayes, Thomas 123–4
Bayesian inference 99
beauty 184–5
Beck, Andy 82, 95
Bell, Joshua 185–6
Berk, Richard 61–2, 64
bias
of judges 70–1, 75
in machines 65–71
societal and cultural 71
biometric measurements 108
blind faith 14–16, 18
Bonin, Pierre-Cédric ‘company baby‘131–3
books 178
boost effect 151, 152
Bratton, Bill 148–50, 152
breast cancer
aggressive screening 94
detecting abnormalities 84, 87, 95
diagnoses 82–4
mammogram screenings 94, 96
over-diagnosis and over-treatment 94–5
research on corpses
92–3
‘in situ’ cancer cells 93
screening algorithms for 87
tumours, unwittingly carrying 93
bridges (route to Jones Beach)
racist 1
unusual features 1
Brixton
fighting 49
looting and violence 49–50
Brooks, Christopher Drew 64, 77
Brown, Joshua 135
browser history see internet browsing history
buffer zone 144
Burgess, Ernest W. 55–6
burglary 150–1
the boost 151, 152
connections with earthquakes 152
the flag 150–1, 152
Caixin Media 45
calculations 8
calculus 8
Caldicott, Dame Fiona 223n48
Cambridge Analytica 39
advertising 42
fake news 42
personality profiles 41–2
techniques 41–2
whistleblowers 42
CAMELYON16 competition 88, 89
cameras 119–20
cancer
benign 94
detection 88–9
and the immune system 93
malignant 94
‘in situ’ 93, 94
uncertainty of tumours 93–4 see also breast cancer
cancer diagnoses study 79–80
Car and Driver magazine 130–1
Carnegie 117
Carnegie Mellon University 115
cars 113–40
driverless see driverless cars see also DARPA (US Defence Advanced Research Projects Agency)
categories of algorithms
association 9
classification 9
filtering 9–10
prioritization 8
Centaur Chess 202
Charts of the Future 148–50
chauffeur mode 139
chess 5–7
Chicago Police Department 158
China 168
citizen scoring system 45–6
breaking trust 46
punishments 46
Sesame Credit 45–6, 168
smallpox inoculation 81
citizen scoring system 45–6
Citroen DS19 116, 116–17
Citymapper 23
classification algorithms 9
Clinical vs Statistical Prediction (Meehl) 21–2