Hello Word: How to Be Humman in the Age of the Machine

1 From a private conversation with the chess grandmaster Jonathan Rowson.

2 Feng-Hsiung Hsu, ‘IBM’s Deep Blue Chess grandmaster chips’, IEEE Micro, vol. 19, no. 2, 1999, pp. 70–81, http://ieeexplore.ieee.org/document/755469/.

3 Garry Kasparov, Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins (London: Hodder & Stoughton, 2017).

4 TheGoodKnight, ‘Deep Blue vs Garry Kasparov Game 2 (1997 Match)’, YouTube, 18 Oct. 2012, https://www.youtube.com/watch?v=3Bd1Q2rOmok&t=2290s.

5 Ibid.

6 Steven Levy, ‘Big Blue’s Hand of God’, Newsweek, 18 May 1997, http://www.newsweek.com/big-blues-hand-god-173076.

7 Kasparov, Deep Thinking, p. 187.

8 Ibid., p. 191.

9 According to Merriam–Webster. The Oxford English Dictionary’s definition makes more of the mathematical nature of algorithms: ‘a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer’.

10 There are lots of different ways you could group algorithms, and I have no doubt that some computer scientists will complain that this list is too simplistic. It’s true that a more exhaustive list would have included several other category headers: mapping, reduction, regression and clustering, to name a few. But in the end, I chose this particular set of categories – from Nicholas Diakopoulos, Algorithmic Accountability Reporting: On the Investigation of Black Boxes (New York: Tow Center for Digital Journalism, Columbia University, 2014) – as it does a great job at covering the basics and offers a useful way to demystify and distil a vast, complex area of study.

11 Kerbobotat, ‘Went to buy a baseball bat on Amazon, they have some interesting suggestions for accessories’, Reddit, 28 Sept. 2013, https://www.reddit.com/r/funny/comments/1nb16l/went_to_buy_a_baseball_bat_on_amazon_they_have/.

12 Sarah Perez, ‘Uber debuts a “smarter” UberPool in Manhattan’, TechCrunch, 22 May 2017, https://techcrunch.com/2017/05/22/uber-debuts-a-smarter-uberpool-in-manhattan/.

13 I say ‘in theory’ deliberately. The reality might be a little different. Some algorithms have been built over years by hundreds, even thousands, of developers, each incrementally adding their own steps to the process. As the lines of code grow, so does the complexity of the system, until the logical threads become like a tangled plate of spaghetti. Eventually, the algorithm becomes impossible to follow, and far too complicated for any one human to understand.
In 2013 Toyota was ordered to pay $3 million in compensation after a fatal crash involving one of its vehicles. The car had accelerated uncontrollably, despite the driver having her foot on the brake rather than the throttle at the time. An expert witness told the jury that an unintended instruction, hidden deep within the vast tangled mess of software, was to blame. See Phil Koopman, A case study of Toyota unintended acceleration and software safety (Pittsburgh: Carnegie Mellon University, 18 Sept. 2014), https://users.ece.cmu.edu/~koopman/pubs/koopman14_toyota_ua_slides.pdf.

14 This illusion (the example here is taken from https://commons.wikimedia.org/wiki/File:Vase_of_rubin.png) is known as Rubin’s vase, after Edgar Rubin, who developed the idea. It is an example of an ambiguous image – right on the border between two shadowed faces looking towards each other, and an image of a white vase. As it’s drawn, it’s fairly easy to switch between the two in your mind, but all it would take is a couple of lines on the picture to push it in one direction or another. Perhaps a faint outline of the eyes on the faces, or the shadow on the neck of the vase.
The dog/car example of image recognition is a similar story. The team found a picture that was right on the cusp between two different classifications and used the smallest perturbation possible to shift the image from one category to another in the eye of the machine.

15 Jiawei Su, Danilo Vasconcellos Vargas and Kouichi Sakurai, ‘One pixel attack for fooling deep neural networks’, arXiv:1719.08864v4 [cs.LG], 22 Feb. 2018, https://arxiv.org/pdf/1710.08864.pdf.

16 Chris Brooke, ‘“I was only following satnav orders” is no defence: driver who ended up teetering on cliff edge convicted of careless driving’, Daily Mail, 16 Sept. 2009, http://www.dailymail.co.uk/news/article-1213891/Driver-ended-teetering-cliff-edge-guilty-blindly-following-sat-nav-directions.html#ixzz59vihbQ2n.

17 Ibid.

18 Robert Epstein and Ronald E. Robertson, ‘The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections’, Proceedings of the National Academy of Sciences, vol. 112, no. 33, 2015, pp. E4512–21, http://www.pnas.org/content/112/33/E4512.

19 David Shultz, ‘Internet search engines may be influencing elections’, Science, 7 Aug. 2015, http://www.sciencemag.org/news/2015/08/internet-search-engines-may-be-influencing-elections.

20 Epstein and Robertson, ‘The search engine manipulation effect (SEME)’.

21 Linda J. Skitka, Kathleen Mosier and Mark D. Burdick, ‘Accountability and automation bias’, International Journal of Human–Computer Studies, vol. 52, 2000, pp. 701–17, http://lskitka.people.uic.edu/IJHCS2000.pdf.

22 KW v. Armstrong, US District Court, D. Idaho, 2 May 2012, https://scholar.google.co.uk/scholar_case?case=17062168494596747089&hl=en&as_sdt=2006.

23 Jay Stanley, Pitfalls of Artificial Intelligence Decision making Highlighted in Idaho ACLU Case, American Civil Liberties Union, 2 June 2017, https://www.aclu.org/blog/privacy-technology/pitfalls-artificial-intelligence-decisionmaking-highlighted-idaho-aclu-case.

24 ‘K.W. v. Armstrong’, Leagle.com, 24 March 2014, https://www.leagle.com/decision/infdco20140326c20.

25 Ibid.

26 ACLU Idaho staff, https://www.acluidaho.org/en/about/staff.

27 Stanley, Pitfalls of Artificial Intelligence Decision-making.

28 ACLU, Ruling mandates important protections for due process rights of Idahoans with developmental disabilities, 30 March 2016, https://www.aclu.org/news/federal-court-rules-against-idaho-department-health-and-welfare-medicaid-class-action.

29 Stanley, Pitfalls of Artificial Intelligence Decision-making.

30 Ibid.

31 Ibid.

32 Ibid.

33 Ibid.

34 Kristine Phillips, ‘The former Soviet officer who trusted his gut – and averted a global nuclear catastrophe’, Washington Post, 18 Sept. 2017, https://www.washingtonpost.com/news/retropolis/wp/2017/09/18/the-former-soviet-officer-who-trusted-his-gut-and-averted-a-global-nuclear-catastrophe/?utm_term=.6546e0f06cce.

35 Pavel Aksenov, ‘Stanislav Petrov: the man who may have saved the world’, BBC News, 26 Sept. 2013, http://www.bbc.co.uk/news/world-europe-24280831.

36 Ibid.

37 Stephen Flanagan, Re: Accident at Smiler Rollercoaster, Alton Towers, 2 June 2015: Expert’s Report, prepared at the request of the Health and Safety Executive, Oct. 2015, http://www.chiark.greenend.org.uk/~ijackson/2016/Expert%20witness%20report%20from%20Steven%20Flanagan.pdf.

38 Paul E. Meehl, Clinical versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence (Minneapolis: University of Minnesota, 1996; first publ. 1954), http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.693.6031&rep=rep1&type=pdf.

39 William M. Grove, David H. Zald, Boyd S. Lebow, Beth E. Snitz and Chad Nelson, ‘Clinical versus mechanical prediction: a meta-analysis’, Psychological Assessment, vol. 12, no. 1, 2000, p. 19.

40 Berkeley J. Dietvorst, Joseph P. Simmons and Cade Massey. ‘Algorithmic aversion: people erroneously avoid algorithms after seeing them err’, Journal of Experimental Psychology, Sept. 2014, http://opim.wharton.upenn.edu/risk/library/WPAF201410-AlgorithmAversion-Dietvorst-Simmons-Massey.pdf.

Data

1 Nicholas Carlson, ‘Well, these new Zuckerberg IMs won’t help Facebook’s privacy problems’, Business Insider, 13 May 2010, http://www.businessinsider.com/well-these-new-zuckerberg-ims-wont-help-facebooks-privacy-problems-2010-5?IR=T.

2 Clive Humby, Terry Hunt and Tim Phillips, Scoring Points: How Tesco Continues to Win Customer Loyalty (London: Kogan Page, 2008).

3 Ibid., Kindle edn, 1313–17.

4 See Eric Schmidt, ‘The creepy line’, YouTube, 11 Feb. 2013, https://www.youtube.com/watch?v=o-rvER6YTss.

5 Charles Duhigg, ‘How companies learn your secrets’, New York Times, 16 Feb. 2012, https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html.

6 Ibid.

7 Sarah Buhr, ‘Palantir has raised $880 million at a $20 billion valuation’, TechCrunch, 23 Dec. 2015.

8 Federal Trade Commission, Data Brokers: A Call for Transparency and Accountability, (Washington DC, May 2014), https://www.ftc.gov/system/files/documents/reports/data-brokers-call-transparency-accountability-report-federal-trade-commission-may-2014/140527databrokerreport.pdf.

9 Ibid.

10 Wolfie Christl, Corporate Surveillance in Everyday Life, Cracked Labs, June 2017, http://crackedlabs.org/en/corporate-surveillance.

11 Heidi Waterhouse, ‘The death of data: retention, rot, and risk’, The Lead Developer, Austin, Texas, 2 March 2018, https://www.youtube.com/watch?v=mXvPChEo9iU.

12 Amit Datta, Michael Carl Tschantz and Anupam Datta, ‘Automated experiments on ad privacy settings’, Proceedings on Privacy Enhancing Technologies, no. 1, 2015, pp. 92–112.

13 Latanya Sweeney, ‘Discrimination in online ad delivery’, Queue, vol. 11, no. 3, 2013, p. 10, https://dl.acm.org/citation.cfm?id=2460278.

14 Jon Brodkin, ‘Senate votes to let ISPs sell your Web browsing history to advertisers’, Ars Technica, 23 March 2017, https://arstechnica.com/tech-policy/2017/03/senate-votes-to-let-isps-sell-your-web-browsing-history-to-advertisers/.

15 Svea Eckert and Andreas Dewes, ‘Dark data’, DEFCON Conference 25, 20 Oct. 2017, https://www.youtube.com/watch?v=1nvYGi7-Lxo.

16 The researchers based this part of their work on Arvind Narayanan and Vitaly Shmatikov, ‘Robust de-anonymization of large sparse datasets’, paper presented to IEEE Symposium on Security and Privacy, 18–22 May 2008.

17 Michal Kosinski, David Stillwell and Thore Graepel. ‘Private traits and attributes are predictable from digital records of human behavior’, vol. 110, no. 15, 2013, pp. 5802–5.

18 Ibid.

19 Wu Youyou, Michal Kosinski and David Stillwell, ‘Computer-based personality judgments are more accurate than those made by humans’, Proceedings of the National Academy of Sciences, vol. 112, no. 4, 2015, pp. 1036–40.

20 S. C. Matz, M. Kosinski, G. Nave and D. J. Stillwell, ‘Psychological targeting as an effective approach to digital mass persuasion’, Proceedings of the National Academy of Sciences, vol. 114, no. 48, 2017, 201710966.

21 Paul Lewis and Paul Hilder, ‘Leaked: Cambridge Analytica’s blueprint for Trump victory’, Guardian, 23 March 2018.

22 ‘Cambridge Analytica planted fake news’, BBC, 20 March 2018, http://www.bbc.co.uk/news/av/world-43472347/cambridge-analytica-planted-fake-news.

23 Adam D. I. Kramer, Jamie E. Guillory and Jeffrey T. Hancock, ‘Experimental evidence of massive-scale emotional contagion through social networks’, Proceedings of the National Academy of Sciences, vol. 111, no. 24, 2014, pp. 8788–90.

24 Jamie Bartlett, ‘Big data is watching you – and it wants your vote’, Spectator, 24 March 2018.

25 Li Xiaoxiao, ‘Ant Financial Subsidiary Starts Offering Individual Credit Scores’, Caixin, 2 March 2015, https://www.caixinglobal.com/2015-03-02/101012655.html.

26 Rick Falkvinge, ‘In China, your credit score is now affected by your political opinions – and your friends’ political opinions’, Privacy News Online, 3 Oct. 2015, https://www.privateinternetaccess.com/blog/2015/10/in-china-your-credit-score-is-now-affected-by-your-political-opinions-and-your-friends-political-opinions/.

27 State Council Guiding Opinions Concerning Establishing and Perfecting Incentives for Promise-keeping and Joint Punishment Systems for Trust-breaking, and Accelerating the Construction of Social Sincerity, China Copyright and Media, 30 May 2016, updated 18 Oct. 2016, https://chinacopyrightandmedia.wordpress.com/2016/05/30/state-council-guiding-opinions-concerning-establishing-and-perfecting-incentives-for-promise-keeping-and-joint-punishment-systems-for-trust-breaking-and-accelerating-the-construction-of-social-sincer/.

28 Rachel Botsman, Who Can You Trust? How Technology Brought Us Together – and Why It Could Drive Us Apart (London: Penguin, 2017), Kindle edn, p. 151.

Justice

1 John-Paul Ford Rojas, ‘London riots: Lidl water thief jailed for six months’, Telegraph, 7 Jan. 2018, http://www.telegraph.co.uk/news/uknews/crime/8695988/London-riots-Lidl-water-thief-jailed-for-six-months.html.

2 Matthew Taylor, ‘London riots: how a peaceful festival in Brixton turned into a looting free-for-all’, Guardian, 8 Aug. 2011, https://www.theguardian.com/uk/2011/aug/08/london-riots-festival-brixton-looting.

3 Rojas, ‘London riots’.

4 Josh Halliday, ‘London riots: how BlackBerry Messenger played a key role’, Guardian, 8 Aug. 2011, https://www.theguardian.com/media/2011/aug/08/london-riots-facebook-twitter-blackberry.

5 David Mills, ‘Paul and Richard Johnson avoid prison over riots’, News Shopper, 13 Jan. 2012, http://www.newsshopper.co.uk/londonriots/9471288.Father_and_son_avoid_prison_over_riots/.

6 Ibid.

7 Rojas, ‘London riots’. ‘Normally, the police wouldn’t arrest you for such an offence. They wouldn’t hold you in custody. They wouldn’t take you to court,’ Hannah Quirk, a senior lecturer in criminal law and justice at Manchester University wrote about Nicholas’s case in 2015: Carly Lightowlers and Hannah Quirk, ‘The 2011 English “riots”: prosecutorial zeal and judicial abandon’, British Journal of Criminology, vol. 55, no. 1, 2015, pp. 65–85.

8 Mills, ‘Paul and Richard Johnson avoid prison over riots’.

9 William Austin and Thomas A. Williams III, ‘A survey of judges’ responses to simulated legal cases: research note on sentencing disparity’, Journal of Criminal Law and Criminology, vol. 68, no. 2, 1977, pp. 306–310.

10 Mandeep K. Dhami and Peter Ayton, ‘Bailing and jailing the fast and frugal way’, Journal of Behavioral Decision-making, vol. 14, no. 2, 2001, pp. 141–68, http://onlinelibrary.wiley.com/doi/10.1002/bdm.371/abstract.

11 Up to half of the judges differed in their opinion of the best course of action on any one case.

12 Statisticians have a way to measure this kind of consistency in judgments, called Cohen’s Kappa. It takes into account the fact that – even if you were just wildly guessing – you could end up being consistent by chance. A score of one means perfect consistency. A score of zero means you’re doing no better than random. The judges’ scores ranged from zero to one and averaged 0.69.

13 Diane Machin, ‘Sentencing guidelines around the world’, paper prepared for Scottish Sentencing Council, May 2005, https://www.scottishsentencingcouncil.org.uk/media/1109/paper-31a-sentencing-guidelines-around-the-world.pdf.

14 Ibid.

15 Ibid.

16 Ernest W. Burgess, ‘Factors determining success or failure on parole’, in The Workings of the Intermediate-sentence Law and Parole System in Illinois (Springfield, IL: State Board of Parole, 1928). It’s a tricky paper to track down, so here is an alternative read by Burgess’s colleague Tibbitts, on the follow-up study to the original: Clark Tibbitts, ‘Success or failure on parole can be predicted: a study of the records of 3,000 youths paroled from the Illinois State Reformatory’, Journal of Criminal Law and Criminology, vol. 22, no. 1, Spring 1931, https://scholarlycommons.law.northwestern.edu/cgi/viewcontent.cgi?article=2211&context=jclc. The other categories used by Burgess were ‘black sheep’, ‘criminal by accident’, ‘dope’ and ‘gangster’. ‘Farm boys’ were the category he found least likely to re-offend.

17 Karl F. Schuessler, ‘Parole prediction: its history and status’, Journal of Criminal Law and Criminology, vol. 45, no. 4, 1955, pp. 425–31, https://pdfs.semanticscholar.org/4cd2/31dd25321a0c14a9358a93ebccb6f15d3169.pdf.

18 Ibid.

19 Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age (Chicago and London: University of Chicago Press, 2007), p. 1.

20 Philip Howard, Brian Francis, Keith Soothill and Les Humphreys, OGRS 3: The Revised Offender Group Reconviction Scale, Research Summary 7/09 (London: Ministry of Justice, 2009), https://core.ac.uk/download/pdf/1556521.pdf.

21 A slight caveat here: there probably is some selection bias in this statistic. ‘Ask the audience’ was typically used in the early rounds of the game, when the questions were a lot easier. None the less, the idea of the collective opinions of a group being more accurate than those of any individual is a well-documented phenomenon. For more on this, see James Surowiecki, The Wisdom of Crowds: Why the Many Are Smarter than the Few (New York: Doubleday, 2004), p. 4.

22 Netflix Technology Blog, https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-2-d9b96aa399f5.

23 Shih-ho Cheng, ‘Unboxing the random forest classifier: the threshold distributions’, Airbnb Engineering and Data Science, https://medium.com/airbnb-engineering/unboxing-the-random-forest-classifier-the-threshold-distributions-22ea2bb58ea6.

24 Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig and Sendhil Mullainathan, Human Decisions and Machine Predictions, NBER Working Paper no. 23180 (Cambridge, MA: National Bureau of Economic Research, Feb. 2017), http://www.nber.org/papers/w23180. This study actually used ‘gradient boosted decision trees’, an algorithm similar to random forests. Both combine the predictions of lots of decision trees to arrive at a decision, but the trees in the gradient-boosted method are grown sequentially, while in random forests they are grown in parallel. To set up this study, the dataset was first chopped in half. One half was used to train the algorithm, the other half was kept to one side. Once the algorithm was ready, it took cases from the half that it had never seen before to try to predict what would happen. (Without splitting the data first, your algorithm would just be a fancy look-up table).

25 Academics have spent time developing statistical techniques to deal with precisely this issue, so that you can still make a meaningful comparison between the respective predictions made by judges and algorithms. For more details on this, see Kleinberg et al., Human Decisions and Machine Predictions.

26 ‘Costs per place and costs per prisoner by individual prison’, National Offender Management Service Annual Report and Accounts 2015–16, Management Information Addendum, Ministry of Justice information release, 27 Oct. 2016, https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/563326/costs-per-place-cost-per-prisoner-2015-16.pdf.

27 Marc Santora, ‘City’s annual cost per inmate is $168,000, study finds’, New York Times, 23 Aug. 2013, http://www.nytimes.com/2013/08/24/nyregion/citys-annual-cost-per-inmate-is-nearly-168000-study-says.html; Harvard University, ‘Harvard at a glance’, https://www.harvard.edu/about-harvard/harvard-glance.

28 Luke Dormehl, The Formula: How Algorithms Solve All Our Problems … and Create More (London: W. H. Allen, 2014), p. 123.

29 Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner, ‘Machine bias’, ProPublica, 23 May 2016, https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.

30 ‘Risk assessment’ questionnaire, https://www.documentcloud.org/documents/2702103-Sample-Risk-Assessment-COMPAS-CORE.html.

31 Tim Brennan, William Dieterich and Beate Ehret (Northpointe Institute), ‘Evaluating the predictive validity of the COMPAS risk and needs assessment system’, Criminal Justice and Behavior, vol. 36, no. 1, 2009, pp. 21–40, http://www.northpointeinc.com/files/publications/Criminal-Justice-Behavior-COMPAS.pdf. According to a 2018 study, the COMPAS algorithm has a similar accuracy to an ‘ensemble’ of humans. The researchers demonstrated that asking a group of 20 inexperienced individuals to predict recidivism achieved an equivalent score to the COMPAS system. It’s an interesting comparison, but it’s worth remembering that in real courts, judges don’t have a team of strangers making votes in the back room. They’re on their own. And that’s the only comparison that really counts. See Julia Dressel and Hany Farid, ‘The accuracy, fairness, and limits of predicting recidivism’, Science Advances, vol. 4, no. 1, 2018.

32 Christopher Drew Brooks v. Commonwealth, Court of Appeals of Virginia, Memorandum Opinion by Judge Rudolph Bumgardner III, 28 Jan. 2004, https://law.justia.com/cases/virginia/court-of-appeals-unpublished/2004/2540023.html.

33 ‘ACLU brief challenges constitutionality of Virginia’s sex offender risk assessment guidelines’, American Civil Liberties Union Virginia, 28 Oct. 2003, https://acluva.org/en/press-releases/aclu-brief-challenges-constitutionality-virginias-sex-offender-risk-assessment.

34 State v. Loomis, Supreme Court of Wisconsin,13 July 2016, http://caselaw.findlaw.com/wi-supreme-court/1742124.html.

35 Quotations from Richard Berk are from personal communication.

36 Angwin et al., ‘Machine bias’.

37 Global Study on Homicide 2013 (Vienna: United Nations Office on Drugs and Crime, 2014), http://www.unodc.org/documents/gsh/pdfs/2014_GLOBAL_HOMICIDE_BOOK_web.pdf.

38ACLU, ‘The war on marijuana in black and white’, June 2013, www.aclu.org/files/assets/aclu-thewaronmarijuana-ve12.pdf

39 Surprisingly, perhaps, Equivant’s stance on this is backed up by the Supreme Court of Wisconsin. After Eric Loomis was sentenced to prison for six years by a judge using the COMPAS risk-assessment tool, he appealed the ruling. The case, Loomis v. Wisconsin, claimed that the use of proprietary, closed-source risk-assessment software to determine his sentence violated his right to due process, because the defence can’t challenge the scientific validity of the score. But the Wisconsin Supreme Court ruled that a trial court’s use of an algorithmic risk assessment in sentencing did not violate the defendant’s due process rights: Supreme Court of Wisconsin, case no. 2015AP157-CR, opinion filed 13 July 2016, https://www.wicourts.gov/sc/opinion/DisplayDocument.pdf?content=pdf&seqNo=171690.

40 Lucy Ward, ‘Why are there so few female maths professors in universities?’, Guardian, 11 March 2013, https://www.theguardian.com/lifeandstyle/the-womens-blog-with-jane-martinson/2013/mar/11/women-maths-professors-uk-universities.

41 Sonja B. Starr and M. Marit Rehavi, Racial Disparity in Federal Criminal Charging and Its Sentencing Consequences, Program in Law and Economics Working Paper no. 12-002 (Ann Arbor: University of Michigan Law School, 7 May 2012), http://economics.ubc.ca/files/2013/05/pdf_paper_marit-rehavi-racial-disparity-federal-criminal.pdf.

42 David Arnold, Will Dobbie and Crystal S. Yang, Racial Bias in Bail Decisions, NBER Working Paper no. 23421 (Cambridge, MA: National Bureau of Economic Research, 2017), https://www.princeton.edu/~wdobbie/files/racialbias.pdf.

43 John J. Donohue III, Capital Punishment in Connecticut, 1973–2007: A Comprehensive Evaluation from 4686 Murders to One Execution (Stanford, CA, and Cambridge, MA: Stanford Law School and National Bureau of Economic Research, Oct. 2011), https://law.stanford.edu/wp-content/uploads/sites/default/files/publication/259986/doc/slspublic/fulltext.pdf.

44 Adam Benforado, Unfair: The New Science of Criminal Injustice (New York: Crown, 2015), p. 197.

45 Sonja B. Starr, Estimating Gender Disparities in Federal Criminal Cases, University of Michigan Law and Economics Research Paper no. 12-018 (Ann Arbor: University of Michigan Law School, 29 Aug. 2012), https://ssrn.com/abstract=2144002 or http://dx.doi.org/10.2139/ssrn.2144002.

46 David B. Mustard, ‘Racial, ethnic, and gender disparities in sentencing: evidence from the US federal courts’, Journal of Law and Economics, vol. 44, no. 2, April 2001, pp. 285–314, http://people.terry.uga.edu/mustard/sentencing.pdf.

47 Daniel Kahneman, Thinking, Fast and Slow (New York: Farrar, Straus & Giroux, 2011), p. 44.

48 Chris Guthrie, Jeffrey J. Rachlinski and Andrew J. Wistrich, Blinking on the Bench: How Judges Decide Cases, paper no. 917 (New York: Cornell University Law Faculty, 2007), http://scholarship.law.cornell.edu/facpub/917.

49 Kahneman, Thinking, Fast and Slow, p. 13.

50 Ibid., p. 415.

51 Dhami and Ayton, ‘Bailing and jailing the fast and frugal way’.

52 Brian Wansink, Robert J. Kent and Stephen J. Hoch, ‘An anchoring and adjustment model of purchase quantity decisions’, Journal of Marketing Research, vol. 35, 1998, pp. 71–81, http://foodpsychology.cornell.edu/sites/default/files/unmanaged_files/Anchoring-JMR-1998.pdf.

53 Mollie Marti and Roselle Wissler, ‘Be careful what you ask for: the effect of anchors on personal injury damages awards’, Journal of Experimental Psychology: Applied, vol. 6, no. 2, 2000, pp. 91–103.

54 Birte Englich and Thomas Mussweiler, ‘Sentencing under uncertainty: anchoring effects in the courtroom’, Journal of Applied Social Psychology, vol. 31, no. 7, 2001, pp. 1535–51, http://onlinelibrary.wiley.com/doi/10.1111/j.1559-1816.2001.tb02687.x.

55 Birte Englich, Thomas Mussweiler and Fritz Strack, ‘Playing dice with criminal sentences: the influence of irrelevant anchors on experts’ judicial decision making’, Personality and Social Psychology Bulletin, vol. 32, 2006, pp. 188–200, https://www.researchgate.net/publication/7389517_Playing_Dice_With_Criminal_Sentences_The_Influence_of_Irrelevant_Anchors_on_Experts%27_Judicial_Decision_Making?enrichId=rgreq-f2fedfeb71aa83f8fad80cc24df3254d-XXX&enrichSource=Y292ZXJQYWdlOzczODk1MTc7QVM6MTAzODIzNjIwMTgyMDIyQDE0MDE3NjQ4ODgzMTA%3D&el=1_x_3&_esc=publicationCoverPdf.

56 Ibid.

57 Ibid.

58 Mandeep K. Dhami, Ian K. Belton, Elizabeth Merrall, Andrew McGrath and Sheila Bird, ‘Sentencing in doses: is individualized justice a myth?’, under review. Kindly shared through personal communication with Mandeep Dhami.

59 Ibid.

60 Adam N. Glynn and Maya Sen, ‘Identifying judicial empathy: does having daughters cause judges to rule for women’s issues?’, American Journal of Political Science, vol. 59, no. 1, 2015, pp. 37–54, https://scholar.harvard.edu/files/msen/files/daughters.pdf.

61 Shai Danziger, Jonathan Levav and Liora Avnaim-Pesso, ‘Extraneous factors in judicial decisions’, Proceedings of the National Academy of Sciences of the United States of America, vol. 108, no. 17, 2011, pp. 6889–92, http://www.pnas.org/content/108/17/6889.

62 Keren Weinshall-Margel and John Shapard, ‘Overlooked factors in the analysis of parole decisions’, Proceedings of the National Academy of Sciences of the United States of America, vol. 108, no. 42, 2011, E833, http://www.pnas.org/content/108/42/E833.long.

63 Uri Simonsohn and Francesca Gino, ‘Daily horizons: evidence of narrow bracketing in judgment from 9,000 MBA-admission interviews’, Psychological Science, vol. 24, no. 2, 2013, pp. 219–24, https://ssrn.com/abstract=2070623.

64 Lawrence E. Williams and John A. Bargh, ‘Experiencing physical warmth promotes interpersonal warmth’, Science, vol. 322, no. 5901, pp. 606–607, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2737341/.

Medicine

1 Richard M. Levenson, Elizabeth A. Krupinski, Victor M. Navarro and Edward A. Wasserman. ‘Pigeons (Columba livia) as trainable observers of pathology and radiology breast cancer images’, PLOSOne, 18 Nov. 2015, http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141357.

2 ‘Hippocrates’ daughter as a dragon kills a knight, in “The Travels of Sir John Mandeville”’, British Library Online Gallery, 26 March 2009, http://www.bl.uk/onlinegallery/onlineex/illmanus/harlmanucoll/h/011hrl000003954u00008v00.html.

3 Eleni Tsiompanou, ‘Hippocrates: timeless still’, JLL Bulletin: Commentaries on the History of Treatment Evaluation (Oxford and Edinburgh: James Lind Library, 2012), http://www.jameslindlibrary.org/articles/hippocrates-timeless-still/.

4 David K. Osborne, ‘Hippocrates: father of medicine’, Greek Medicine.net, 2015, http://www.greekmedicine.net/whos_who/Hippocrates.html.

5 Richard Colgan, ‘Is there room for art in evidence-based medicine?’, AMA Journal of Ethics, Virtual Mentor 13: 1, Jan. 2011, pp. 52–4, http://journalofethics.ama-assn.org/2011/01/msoc1-1101.html.

6 Joseph Needham, Science and Civilization in China, vol. 6, Biology and Biological Technology, part VI, Medicine, ed. Nathan Sivin (Cambridge: Cambridge University Press, 2004), p. 143, https://monoskop.org/images/1/16/Needham_Joseph_Science_and_Civilisation_in_China_Vol_6-6_Biology_and_Biological_Technology_Medicine.pdf.

7 ‘Ignaz Semmelweis’, Brought to Life: Exploring the History of Medicine (London: Science Museum n.d.), http://broughttolife.sciencemuseum.org.uk/broughttolife/people/ignazsemmelweis.

8 Quotations from Andy Beck are from personal communication.

9 Joann G. Elmore, Gary M. Longton, Patricia A. Carney, Berta M. Geller, Tracy Onega, Anna N. A. Tosteson, Heidi D. Nelson, Margaret S. Pepe, Kimberly H. Allison, Stuart J. Schnitt, Frances P. O’Malley and Donald L. Weaver, ‘Diagnostic concordance among pathologists interpreting breast biopsy specimens’, Journal of the American Medical Association, vol. 313, no. 11, 17 March 2015, 1122–32, https://jamanetwork.com/journals/jama/fullarticle/2203798.

10 Ibid.

11 The name ‘neural network’ came about as an analogy with what happens in the brain. There, billions of neurons are connected to one another in a gigantic network. Each neuron listens to its connections and sends out a signal whenever it picks up on another neuron being excited. The signal then excites some other neurons that are listening to it.
  A neural network is a much simpler and more orderly version of the brain. Its (artificial) neurons are structured in layers, and all the neurons in each layer listen to all the neurons in the previous layer. In our dog example the very first layer is the individual pixels in the image. Then there are several layers with thousands of neurons in them, and a final layer with only a single neuron in it that outputs the probability that the image fed in is a dog.
  The procedure for updating the neurons is known as the ‘backpropagation algorithm’. We start with the final neuron that outputs the probability that the image is a dog. Let’s say we fed in an image of a dog and it predicted that the image had a 70 per cent chance of being a dog. It looks at the signals it received from the previous layer and says, ‘The next time I receive information like that I’ll increase my probability that the image is a dog’. It then says to each of the neurons in the previous layer, ‘Hey, if you’d given me this signal instead I would have made a better prediction’. Each of those neurons looks at its input signals and changes what it would output the next time. And then it tells the previous layer what signals it should have sent, and so on through all the layers back to the beginning. It is this process of propagating the errors back through the neural network that leads to the name ‘the backpropagation algorithm’.
  For a more detailed overview of neural networks, how they are built and trained, see Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (New York: Basic Books, 2015).

12 Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton, ‘ImageNet classification with deep convolutional neural networks’, in F. Pereira, C. J. C. Burges, L. Bottou and K. Q. Weinberger, eds, Advances in Neural Information Processing Systems 25 (La Jolla, CA, Neural Information Processing Systems Foundation, 2012), pp. 1097–1105, http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf. This particular algorithm is known as a convolutional neural network. Rather than the entire image being fed in, the algorithm first applies a host of different filters and looks for local patterns in the way the picture is distorted.

13 Marco Tulio Ribeiro, Sameer Singh and Carlos Guestrin, ‘“Why should I trust you?” Explaining the predictions of any classifier’, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 2016, pp. 1135–44, http://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.pdf.

14 This was compared to the assessment of a panel of experts, whose collective analysis was considered to be the ‘ground truth’ for what was contained in the slides.

15 Trafton Drew, Melissa L. H. Vo and Jeremy M. Wolfe, ‘The invisible gorilla strikes again: sustained inattentional blindness in expert observers’, Psychological Science, vol. 24, no. 9, Sept. 2013, pp. 1848–53, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3964612/.

16 The gorilla is located in the top-right-hand side of the image.

17 Yun Liu, Krishna Gadepalli, Mohammad Norouzi, George E. Dahl, Timo Kohlberger, Aleksey Boyko, Subhashini Venugopalan, Aleksei Timofeev, Philip Q. Nelson, Greg S. Corrado, Jason D. Hipp, Lily Peng and Martin C. Stumpe, ‘Detecting cancer metastases on gigapixel pathology images’, Cornell University Library, 8 March 2017, https://arxiv.org/abs/1703.02442.

18 Dayong Wang, Aditya Khosla, Rishab Gargeya, Humayun Irshad and Andrew H. Beck, ‘Deep learning for identifying metastatic breast cancer’, Cornell University Library, 18 June 2016, https://arxiv.org/abs/1606.05718.

19 David A. Snowdon, ‘The Nun Study’, Boletin de LAZOS de la Asociación Alzheimer de Monterrey, vol. 4, no. 22, 2000; D. A. Snowdon, ‘Healthy aging and dementia: findings from the Nun Study’, Annals of Internal Medicine, vol. 139, no. 5, Sept. 2003, pp. 450–54.

20 The idea density – a proxy for linguistic complexity – was calculated by counting up the number of unique ideas each nun used per string of ten words. There’s a nice overview here: Associated Press, ‘Study of nuns links early verbal skills to Alzheimer’s, Los Angeles Times, 21 Feb. 1996, http://articles.latimes.com/1996-02-21/news/mn-38356_1_alzheimer-nuns-studied.

21 Maja Nielsen, Jørn Jensen and Johan Andersen, ‘Pre-cancerous and cancerous breast lesions during lifetime and at autopsy: a study of 83 women’, Cancer, vol. 54, no. 4, 1984, pp. 612–15, http://onlinelibrary.wiley.com/wol1/doi/10.1002/1097-0142(1984)54:4%3C612::AID-CNCR2820540403%3E3.0.CO;2-B/abstract.

22 H. Gilbert Welch and William C. Black, ‘Using autopsy series to estimate the disease “reservoir” for ductal carcinoma in situ of the breast: how much more breast cancer can we find?’, Annals of Internal Medicine, vol. 127, no. 11, Dec. 1997, pp. 1023–8, www.vaoutcomes.org/papers/Autopsy_Series.pdf.

23 Getting an exact statistic is tricky because it depends on the country and demographic (and how aggressively your country screens for breast cancer). For a good summary, see: http://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/breast-cancer.

24 Quotations from Jonathan Kanevsky are from personal communication.

25 ‘Breakthrough method predicts risk of DCIS becoming invasive breast cancer’, Artemis, May 2010, http://www.hopkinsbreastcenter.org/artemis/201005/3.html.

26 H. Gilbert Welch, Philip C. Prorok, A. James O’Malley and Barnett S. Kramer, ‘Breast-cancer tumor size, overdiagnosis, and mammography screening effectiveness’, New England Journal of Medicine, vol. 375, 2016, pp. 1438–47, http://www.nejm.org/doi/full/10.1056/NEJMoa1600249.

27 Independent UK Panel on Breast Cancer Screening, ‘The benefits and harms of breast cancer screening: an independent review’, Lancet, vol. 380, no. 9855, 30 Oct. 2012, pp. 1778–86, http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(12)61611-0/abstract.

28 Personal communication.

29 Andrew H. Beck, Ankur R. Sangoi, Samuel Leung, Robert J. Marinelli, Torsten O. Nielsen, Marc J. van de Vijver, Robert B. West, Matt van de Rijn and Daphne Koller, ‘Systematic analysis of breast cancer morphology uncovers stromal features associated with survival’, Science Transitional Medicine, 19 Dec. 2014, https://becklab.hms.harvard.edu/files/becklab/files/sci_transl_med-2011-beck-108ra113.pdf.

30 Phi Vu Tran, ‘A fully convolutional neural network for cardiac segmentation in short-axis MRI’, 27 April 2017, https://arxiv.org/pdf/1604.00494.pdf.

31 ‘Emphysema’, Imaging Analytics, Zebra Medical, https://www.zebra-med.com/algorithms/lungs/.

32 Eun-Jae Lee, Yong-Hwan Kim, Dong-Wha Kang et al., ‘Deep into the brain: artificial intelligence in stroke imaging’, Journal of Stroke, vol. 19, no. 3, 2017, pp. 277–85, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5647643/.

33 Taylor Kubota, ‘Deep learning algorithm does as well as dermatologists in identifying skin cancer’, Stanford News, 25 Jan. 2017, https://news.stanford.edu/2017/01/25/artificial-intelligence-used-identify-skin-cancer/.

34 Jo Best, ‘IBM Watson: the inside story of how the Jeopardy-winning supercomputer was born, and what it wants to do next’, Tech Republic, n.d., https://www.techrepublic.com/article/ibm-watson-the-inside-story-of-how-the-jeopardy-winning-supercomputer-was-born-and-what-it-wants-to-do-next/.

35 Jennings Brown, ‘Why everyone is hating on IBM Watson, including the people who helped make it’, Gizmodo, 14 Aug. 2017, https://www.gizmodo.com.au/2017/08/why-everyone-is-hating-on-watsonincluding-the-people-who-helped-make-it/.

36 https://www.theregister.co.uk/2017/02/20/watson_cancerbusting_trial_on_hold_after_damning_audit_report/

37 Casey Ross and Ike Swetlitz, ‘IBM pitched its Watson supercomputer as a revolution in cancer care. It’s nowhere close’, STAT, 5 Sept. 2017, https://www.statnews.com/2017/09/05/watson-ibm-cancer/.

38 Tomoko Otake, ‘Big data used for rapid diagnosis of rare leukemia case in Japan’, Japan Times, 11 Aug. 2016, https://www.japantimes.co.jp/news/2016/08/11/national/science-health/ibm-big-data-used-for-rapid-diagnosis-of-rare-leukemia-case-in-japan/#.Wf8S_hO0MQ8.

39 ‘Researchers validate five new genes responsible for ALS’, Science Daily, 1 Dec. 2017, https://www.sciencedaily.com/releases/2017/12/171201104101.htm.

40 John Freedman, ‘A reality check for IBM’s AI ambitions’, MIT Technology Review, 27 June 2017.

41 Asthma facts and statistics, Asthma UK, 2016, https://www.asthma.org.uk/about/media/facts-and-statistics/; Asthma in the US, Centers for Disease Control and Prevention, May 2011, https://www.cdc.gov/vitalsigns/asthma/index.html.

42 ‘Schoolgirl, 13, who died of asthma attack was making regular trips to A&E and running out of medication – but was NEVER referred to a specialist even when her lips turned blue, mother tells inquest’, Daily Mail, 13 Oct. 2015, http://www.dailymail.co.uk/news/article-3270728/Schoolgirl-13-died-asthma-attack-not-referred-specialist-lips-turned-blue.html.

43 My Data, My Care: How Better Use of Data Improves Health and Wellbeing (London: Richmond Group of Charities, Jan. 2017), https://richmondgroupofcharities.org.uk/publications.

44 Terence Carney, ‘Regulation 28: report to prevent future deaths’, coroner’s report on the case of Tamara Mills, 29 Oct. 2015, https://www.judiciary.gov.uk/publications/tamara-mills/.

45 Jamie Grierson and Alex Hern, ‘Doctors using Snapchat to send patient scans to each other, panel finds’, Guardian, 5 July 2017, https://www.theguardian.com/technology/2017/jul/05/doctors-using-snapchat-to-send-patient-scans-to-each-other-panel-finds.

46 Even if you get around all of those issues, sometimes the data itself just doesn’t exist. There are thousands of rare diseases with an underlying genetic cause that are effectively unique. Doctors have enormous difficulty spotting one of these conditions because in many cases they will have never seen it before. All the algorithms in the world won’t solve issues with tiny sample sizes.

47 Hal Hodson, ‘Revealed: Google AI has access to huge haul of NHS patient data’, New Scientist, 29 April 2016, https://www.newscientist.com/article/2086454-revealed-google-ai-has-access-to-huge-haul-of-nhs-patient-data/.

48 Actually, much of the blame for this so-called ‘legally inappropriate’ deal has been laid at the door of the Royal Free Trust, which was probably a bit too eager to partner up with the most famous artificial intelligence company in the world. See the letter from Dame Fiona Caldicott, the national data guardian, that was leaked to Sky News: Alex Martin, ‘Google received 1.6 million NHS patients’ data on an “inappropriate legal basis”’, Sky News, 15 May 2017, https://photos.google.com/share/AF1QipMdd5VTK0RNQ1AC3Dda1526CMG0vPD4P3x4x6_qmj0Zf101rbKyxfkfyputSPvqdA/photo/AF1QipP1_rnJMXkRyy3IuFHasilQHYEknKgnHFOFEy4T?key=U2pZUDM4bmo5RHhKYVptaDlkbEhfVFh4Rm1iVUVR.

49 Denis Campbell, ‘Surgeons attack plans to delay treatment to obese patients and smokers’, Guardian, 29 Nov. 2016, https://www.theguardian.com/society/2016/nov/29/surgeons-nhs-delay-treatment-obese-patients-smokers-york.

50 Nir Eyal, ‘Denial of treatment to obese patients: the wrong policy on personal responsibility for health’, International Journal of Health Policy and Management, vol. 1, no. 2, Aug. 2013, pp. 107–10, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3937915/.

51 For a description of the procedures, see http://galton.org/essays/1880-1889/galton-1884-jaigi-anthro-lab.pdf.

52 Francis Galton, ‘On the Anthropometric Laboratory at the late international health exhibition’, Journal of the Anthropological Institute of Great Britain and Ireland, vol. 14, 1885, pp. 205–21.

53 ‘Taste’, https://permalinks.23andme.com/pdf/samplereport_traits.pdf.

54 ‘Sneezing on summer solstice?’, 23andMeBlog, 20 June 2012, https://blog.23andme.com/health-traits/sneezing-on-summer-solstice/.

55 ‘Find out what your DNA says about your health, traits and ancestry’, 23andMe, https://www.23andme.com/en-gb/dna-health-ancestry/.

56 Kristen v. Brown, ‘23andMe is selling your data but not how you think’, Gizmodo, 14 April 2017, https://gizmodo.com/23andme-is-selling-your-data-but-not-how-you-think-1794340474.

57 Michael Grothaus, ‘How23andMe is monetizing your DNA’, Fast Company, 15 Jan. 2015, https://www.fastcompany.com/3040356/what-23andme-is-doing-with-all-that-dna.

58 Rob Stein, ‘Found on the Web, with DNA: a boy’s father’, Washington Post, 13 Nov. 2005, http://www.washingtonpost.com/wp-dyn/content/article/2005/11/12/AR2005111200958.html.

59 After having his DNA tested, the young man learned that a particular pattern on his Y-chromosome – passed from father to son – was also shared by two people with the same surname (distant relatives on his father’s side). That surname, together with the place and date of birth of his father, was enough to track him down.

60 M. Gymrek, A. L. McGuire, D. Golan, E. Halperin and Y. Erlich, ‘Identifying personal genomes by surname inference’, Science, vol. 339, no. 6117, Jan. 2013, pp. 321–4, https://www.ncbi.nlm.nih.gov/pubmed/23329047.

61 Currently, genetic tests for Huntington’s disease are not available from any commercial DNA testing kits.

62 Matthew Herper, ‘23andMe rides again: FDA clears genetic tests to predict disease risk’, Forbes, 6 April 2017, https://www.forbes.com/sites/matthewherper/2017/04/06/23andme-rides-again-fda-clears-genetic-tests-to-predict-disease-risk/#302aea624fdc.

Cars

1 DARPA, Grand Challenge 2004: Final Report (Arlington, VA: Defence Advanced Research Projects Agency, 30 July 2004), http://www.esd.whs.mil/Portals/54/Documents/FOID/Reading%20Room/DARPA/15-F-0059_GC_2004_FINAL_RPT_7-30-2004.pdf.

2 The Worldwide Guide to Movie Locations, 7 Sept. 2014, http://www.movie-locations.com/movies/k/Kill_Bill_Vol_2.html#.WkYiqrTQoQ8.

3 Mariella Moon, What you need to know about DARPA, the Pentagon’s mad science division, Engadget, 7 July 2014, https://www.engadget.com/2014/07/07/darpa-explainer/.

4 DARPA, Urban Challenge: Overview, http://archive.darpa.mil/grandchallenge/overview.html.

5 Sebastian Thrun, ‘Winning the DARPA Grand Challenge, 2 August 2006’, YouTube, 8 Oct. 2007, https://www.youtube.com/watch?v=j8zj5lBpFTY.

6 DARPA, Urban Challenge: Overview.

7 ‘DARPA Grand Challenge 2004 – road to …’ , YouTube, 22 Jan. 2014, https://www.youtube.com/watch?v=FaBJ5sPPmcI.

8 Alex Davies, ‘An oral history of the DARPA Grand Challenge, the grueling robot race that launched the self-driving car’, Wired, 8 March 2017, https://www.wired.com/story/darpa-grand-challenge-2004-oral-history/ .

9 ‘Desert race too tough for robots’, BBC News, 15 March, 2004, http://news.bbc.co.uk/1/hi/technology/3512270.stm.

10 Davies, ‘An oral history of the DARPA Grand Challenge’.

11 Denise Chow, ‘DARPA and drone cars: how the US military spawned self-driving car revolution’, LiveScience, 21 March 2014, https://www.livescience.com/44272-darpa-self-driving-car-revolution.html.

12 Joseph Hooper, ‘From Darpa Grand Challenge 2004 DARPA’s debacle in the desert’, Popular Science, 4 June 2004, https://www.popsci.com/scitech/article/2004-06/darpa-grand-challenge-2004darpas-debacle-desert.

13 Davies, ‘An oral history of the DARPA Grand Challenge’.

14 DARPA, Report to Congress: DARPA Prize Authority. Fiscal Year 2005 Report in Accordance with 10 U.S.C. 2374a, March 2006, http://archive.darpa.mil/grandchallenge/docs/grand_challenge_2005_report_to_congress.pdf.

15 Alan Ohnsman, ‘Bosch and Daimler to partner to get driverless taxis to market by early 2020s’, Forbes, 4 April 2017, https://www.forbes.com/sites/alanohnsman/2017/04/04/bosch-and-daimler-partner-to-get-driverless-taxis-to-market-by-early-2020s/#306ec7e63c4b.

16 Ford, Looking Further: Ford Will Have a Fully Autonomous Vehicle in Operation by 2021, https://corporate.ford.com/innovation/autonomous-2021.html.

17 John Markoff, ‘Should your driverless car hit a pedestrian to save your life?’, New York Times, 23 June 2016, https://www.nytimes.com/2016/06/24/technology/should-your-driverless-car-hit-a-pedestrian-to-save-your-life.html.

18 Clive Thompson, Anna Wiener, Ferris Jabr, Rahawa Haile, Geoff Manaugh, Jamie Lauren Keiles, Jennifer Kahn and Malia Wollan, ‘Full tilt: when 100 per cent of cars are autonomous’, New York Times, 8 Nov. 2017, https://www.nytimes.com/interactive/2017/11/08/magazine/tech-design-autonomous-future-cars-100-percent-augmented-reality-policing.html#the-end-of-roadkill.

19 Peter Campbell, ‘Trucks headed for a driverless future: unions warn that millions of drivers’ jobs will be disrupted’, Financial Times, 31 Jan. 2018, https://www.ft.com/content/7686ea3e-e0dd-11e7-a0d4-0944c5f49e46.

20 Markus Maurer, J. Christian Gerdes, Barbara Lenz and Hermann Winner, Autonomous Driving: Technical, Legal and Social Aspects (New York: Springer, May 2016), p 48.

21 Stephen Zavestoski and Julian Agyeman, Incomplete Streets: Processes, Practices, and Possibilities (London: Routledge, 2015), p. 29.

22 Maurer et al., Autonomous Driving, p. 53.

23 David Rooney, Self-guided Cars (London: Science Museum, 27 Aug. 2009), https://blog.sciencemuseum.org.uk/self-guided-cars/.

24 Blake Z. Rong, ‘How Mercedes sees into the future’, Autoweek, 22 Jan. 2014, http://autoweek.com/article/car-news/how-mercedes-sees-future.

25 Dean A. Pomerleau, ALVINN: An Autonomous Land Vehicle In a Neural Network, CMU-CS-89-107 (Pittsburgh: Carnegie Mellon University, Jan. 1989), http://repository.cmu.edu/cgi/viewcontent.cgi?article=2874&context=compsci.

26 Joshua Davis, ‘Say hello to Stanley’, Wired, 1 Jan. 2006, https://www.wired.com/2006/01/stanley/; and, for more detail, Dean A. Pomerleau, Neural Network Perception for Mobile Robot Guidance (New York: Springer, 2012), p. 52.

27 A. Filgueira, H. González-Jorge, S. Lagüela, L. Diaz-Vilariño and P. Arias, ‘Quantifying the influence of rain in LiDAR performance’, Measurement, vol. 95, Jan. 2017, pp. 143–8, DOI: https://doi.org/10.1016/j.measurement.2016.10.009; https://www.sciencedirect.com/science/article/pii/S0263224116305577.

28 Chris Williams, ‘Stop lights, sunsets, junctions are tough work for Google’s robo-cars’, The Register, 24 Aug. 2016, https://www.theregister.co.uk/2016/08/24/google_self_driving_car_problems/.

29 Novatel, IMU Errors and Their Effects, https://www.novatel.com/assets/Documents/Bulletins/APN064.pdf.

30 The theorem itself is just an equation, linking the probability of a hypothesis, given some observed pieces of evidence, and the probability of that evidence, given the hypothesis. A more comprehensive introductory overview can be found at https://arbital.com/p/bayes_rule/?l=1zq.

31 Sharon Bertsch McGrayne, The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy (New Haven: Yale University Press, 2011).

32 M. Bayes and M. Price, An Essay towards Solving a Problem in the Doctrine of Chances. By the Late Rev. Mr. Bayes, F.R.S. Communicated by Mr. Price, in a Letter to John Canton, A.M.F.R.S. (1763), digital copy uploaded to archive.org 2 Aug. 2011, https://archive.org/details/philtrans09948070.

33 Michael Taylor, ‘Self-driving Mercedes-Benzes will prioritize occupant safety over pedestrians’, Car and Driver, 7 Oct. 2016, https://blog.caranddriver.com/self-driving-mercedes-will-prioritize-occupant-safety-over-pedestrians/.

34 Jason Kottke, Mercedes’ Solution to the Trolley Problem, Kottke.org, 24 Oct. 2016, https://kottke.org/16/10/mercedes-solution-to-the-trolley-problem.

35 Jean-François Bonnefon, Azim Shariff and Iyad Rahwan (2016), ‘The social dilemma of autonomous vehicles’, Science, vol. 35, 24 June 2016, DOI 10.1126/science.aaf2654; https://arxiv.org/pdf/1510.03346.pdf.

36 All quotes from Paul Newman are from private conversation.

37 Naaman Zhou, ‘Volvo admits its self-driving cars are confused by kangaroos’, Guardian, 1 July 2017, https://www.theguardian.com/technology/2017/jul/01/volvo-admits-its-self-driving-cars-are-confused-by-kangaroos.

38 All quotes from Jack Stilgoe are from private conversation.

39 Jeff Sabatini, ‘The one simple reason nobody is talking realistically about driverless cars’, Car and Driver, Oct. 2017, https://www.caranddriver.com/features/the-one-reason-nobody-is-talking-realistically-about-driverless-cars-feature.

40 William Langewiesche, ‘The human factor’, Vanity Fair, 17 Sept. 2014, https://www.vanityfair.com/news/business/2014/10/air-france-flight-447-crash.

41 Bureau d’Enquêtes et d’Analyses pour la Sécuritié de l’Aviation Civile, Final Report on the Accident on 1st June 2009 to the Airbus A330-203 registered F-GZCP operated by Air France Flight AF447 Rio de Janeiro – Paris, Eng. edn (Paris, updated July 2012), https://www.bea.aero/docspa/2009/f-cp090601.en/pdf/f-cp090601.en.pdf.

42 Ibid.

43 Langewiesche, ‘The human factor’.

44 Ibid.

45 Jeff Wise, ‘What really happened aboard Air France 447’, Popular Mechanics, 6 Dec. 2011, http://www.popularmechanics.com/flight/a3115/what-really-happened-aboard-air-france-447-6611877/.

46 Langewiesche, ‘The human factor’.

47 Wise, ‘What really happened aboard Air France 447’.

48 Lisanne Bainbridge, ‘Ironies of automation’, Automatica, vol. 19, no. 6, Nov. 1983, pp. 775–9, https://www.sciencedirect.com/science/article/pii/0005109883900468.

49 Ibid.

50 Alex Davies, ‘Everyone wants a level 5 self-driving car – here’s what that means’, Wired, 26 July 2016.

51 Justin Hughes, ‘Car autonomy levels explained’, The Drive, 3 Nov. 2017, http://www.thedrive.com/sheetmetal/15724/what-are-these-levels-of-autonomy-anyway.

52 Bainbridge, ‘Ironies of automation’.

53 Jack Stilgoe, ‘Machine learning, social learning and the governance of self-driving cars’, Social Studies of Science, vol. 48, no. 1, 2017, pp. 25–56.

54 Eric Tingwall, ‘Where are autonomous cars right now? Four systems tested’, Car and Driver, Oct. 2017, https://www.caranddriver.com/features/where-are-autonomous-cars-right-now-four-systems-tested-feature.

55 Tracey Lindeman, ‘Using an orange to fool Tesla’s autopilot is probably a really bad idea’, Motherboard, 16 Jan. 2018, https://motherboard.vice.com/en_us/article/a3na9p/tesla-autosteer-orange-hack.

56 Daisuke Wakabayashi, ‘Uber’s self-driving cars were struggling before Arizona Crash’, New York Times, 23 March 2018, https://www.nytimes.com/2018/03/23/technology/uber-self-driving-cars-arizona.html.

57 Sam Levin, ‘Video released of Uber self-driving crash that killed woman in Arizona’, Guardian, 22 March 2018, https://www.theguardian.com/technology/2018/mar/22/video-released-of-uber-self-driving-crash-that-killed-woman-in-arizona.

58 Audi, The Audi vision of autonomous driving, Audi Newsroom, 11 Sept. 2017, https://media.audiusa.com/en-us/releases/184.

59 P. Morgan, C. Alford and G. Parkhurst, Handover Issues in Autonomous Driving: A Literature Review. Project Report (Bristol: University of the West of England, June 2016), http://eprints.uwe.ac.uk/29167/1/Venturer_WP5.2Lit%20ReviewHandover.pdf.

60 Langewiesche, ‘The human factor’.

61 Evan Ackerman, ‘Toyota’s Gill Pratt on self-driving cars and the reality of full autonomy’, IEEE Spectrum, 23 Jan. 2017, https://spectrum.ieee.org/cars-that-think/transportation/self-driving/toyota-gill-pratt-on-the-reality-of-full-autonomy.

62 Julia Pyper, ‘Self-driving cars could cut greenhouse gas pollution’, Scientific American, 15 Sept. 2014, https://www.scientificamerican.com/article/self-driving-cars-could-cut-greenhouse-gas-pollution/.

63 Raphael E. Stern et al., ‘Dissipation of stop-and-go waves via control of autonomous vehicles: field experiments’, arXiv: 1705.01693v1, 4 May 2017, https://arxiv.org/abs/1705.01693.

64 SomeJoe7777, ‘Tesla Model S forward collision warning saves the day’, YouTube, 19 Oct. 2016, https://www.youtube.com/watch?v=SnRp56XjV_M.

65 Jordan Golson and Dieter Bohn, ‘All new Tesla cars now have hardware for “full self-driving capabilities”: but some safety features will be disabled initially’, The Verge, 19 Oct. 2016, https://www.theverge.com/2016/10/19/13340938/tesla-autopilot-update-model-3-elon-musk-update.

66 Fred Lambert, ‘Tesla introduces first phase of “Enhanced Autopilot”: “measured and cautious for next several hundred million miles” – release notes’, Electrek, 1 Jan 2017, https://electrek.co/2017/01/01/tesla-enhanced-autopilot-release-notes/.

67 DPC Cars, ‘Toyota Guardian and Chauffeur autonomous vehicle platform’, YouTube, 27 Sept. 2017, https://www.youtube.com/watch?v=IMdceKGJ9Oc.

68 Brian Milligan, ‘The most significant development since the safety belt’, BBC News, 15 April 2018, http://www.bbc.co.uk/news/business-43752226.

Crime

1 Bob Taylor, Crimebuster: Inside the Minds of Britain’s Most Evil Criminals (London: Piatkus, 2002), ch. 9, ‘A day out from jail’.

2 Ibid.

3 Nick Davies, ‘Dangerous, in prison – but free to rape’, Guardian, 5 Oct. 1999, https://www.theguardian.com/uk/1999/oct/05/nickdavies1.

4 João Medeiros, ‘How geographic profiling helps find serial criminals’, Wired, Nov. 2014, http://www.wired.co.uk/article/mapping-murder.

5 Nicole H. Rafter, ed., The Origins of Criminology: A Reader (Abingdon: Routledge, 2009), p. 271.

6 Luke Dormehl, The Formula: How Algorithms Solve All Our Problems … and Create More (London: W. H. Allen, 2014), p. 117.

7 Dormehl, The Formula, p. 116.

8 D. Kim Rossmo, ‘Geographic profiling’, in Gerben Bruinsma and David Weisburd, eds, Encyclopedia of Criminology and Criminal Justice (New York: Springer, 2014), https://link.springer.com/referenceworkentry/10.1007%2F978-1-4614-5690-2_678.

9 Ibid.

10 João Medeiros, ‘How geographic profiling helps find serial criminals’.

11 Ibid.

12 ‘“Sadistic” serial rapist sentenced to eight life terms’, Independent (Ireland), 6 Oct. 1999, http://www.independent.ie/world-news/sadistic-serial-rapist-sentenced-to-eight-life-terms-26134260.html.

13 Ibid.

14 Steven C. Le Comber, D. Kim Rossmo, Ali N. Hassan, Douglas O. Fuller and John C. Beier, ‘Geographic profiling as a novel spatial tool for targeting infectious disease control’, International Journal of Health Geographics, vol. 10, no.1, 2011, p. 35, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3123167/.

15 Michelle V. Hauge, Mark D. Stevenson, D. Kim Rossmo and Steven C. Le Comber, ‘Tagging Banksy: using geographic profiling to investigate a modern art mystery’, Journal of Spatial Science, vol. 61, no. 1, 2016, pp. 185–90, http://www.tandfonline.com/doi/abs/10.1080/14498596.2016.1138246.

16 Raymond Dussault, ‘Jack Maple: betting on intelligence’, Government Technology, 31 March 1999, http://www.govtech.com/featured/Jack-Maple-Betting-on-Intelligence.html.

17 Ibid.

18 Ibid.

19 Nicole Gelinas, ‘How Bratton’s NYPD saved the subway system’, New York Post, 6 Aug. 2016, http://nypost.com/2016/08/06/how-brattons-nypd-saved-the-subway-system/.

20 Dussault, ‘Jack Maple: betting on intelligence’.

21 Andrew Guthrie Ferguson, ‘Predictive policing and reasonable suspicion’, Emory Law Journal, vol. 62, no. 2, 2012, p. 259, http://law.emory.edu/elj/content/volume-62/issue-2/articles/predicting-policing-and-reasonable-suspicion.html.

22 Lawrence W. Sherman, Patrick R. Gartin and Michael E. Buerger, ‘Hot spots of predatory crime: routine activities and the criminology of place’, Criminology, vol. 27, no. 1, 1989, pp. 27–56, http://onlinelibrary.wiley.com/doi/10.1111/j.1745-9125.1989.tb00862.x/abstract.

23 Toby Davies and Shane D. Johnson, ‘Examining the relationship between road structure and burglary risk via quantitative network analysis’, Journal of Quantitative Criminology, vol. 31, no. 3, 2015, pp. 481–507, http://discovery.ucl.ac.uk/1456293/5/Johnson_art%253A10.1007%252Fs10940-014-9235-4.pdf.

24 Michael J. Frith, Shane D. Johnson and Hannah M. Fry, ‘Role of the street network in burglars’ spatial decision-making’, Criminology, vol. 55, no. 2, 2017, pp. 344–76, http://onlinelibrary.wiley.com/doi/10.1111/1745-9125.12133/full.

25 Spencer Chainey, Predictive Mapping (Predictive Policing), JDI Brief (London: Jill Dando Institute of Security and Crime Science, University College London, 2012), http://discovery.ucl.ac.uk/1344080/3/JDIBriefs_PredictiveMappingSChaineyApril2012.pdf

26 Ibid.

27 A slight disclaimer. The PredPol algorithm itself isn’t publicly available. The experiment we’re referring to here was conducted by the same mathematicians who founded PredPol, using a technique that matches up to how the proprietary software is described. All the clues suggest they’re the same thing, but strictly speaking we can’t be absolutely sure.

28 G. O. Mohler, M. B. Short, Sean Malinowski, Mark Johnson, G. E. Tita, Andrea L. Bertozzi and P. J. Brantingham, ‘Randomized controlled field trials of predictive policing’, Journal of the American Statistical Association, vol. 110, no. 512, 2015, pp. 1399–1411, http://www.tandfonline.com/doi/abs/10.1080/01621459.2015.1077710.

29 Kent Police Corporate Services Analysis Department, PredPol Operational Review, 2014, http://www.statewatch.org/docbin/uk-2014-kent-police-predpol-op-review.pdf.

30 Mohler et al., ‘Randomized controlled field trials of predictive policing’.

31 Kent Police Corporate Services Analysis Department, PredPol Operational Review: Initial Findings, 2013, https://www.whatdotheyknow.com/request/181341/response/454199/attach/3/13%2010%20888%20Appendix.pdf.

32 Kent Police Corporate Services Analysis Department, PredPol Operational Review.

33 This wasn’t actually PredPol, but a much simpler algorithm that also used the ideas of the ‘flag’ and ‘boost’ effects. See Matthew Fielding and Vincent Jones, ‘Disrupting the optimal forager: predictive risk mapping and domestic burglary reduction in Trafford, Greater Manchester’, International Journal of Police Science and Management, vol. 14, no. 1, 2012, pp. 30–41.

34 Joe Newbold, ‘“Predictive policing”, “preventative policing” or “intelligence led policing”. What is the future?’ Consultancy project submitted in assessment for Warwick MBA programme, Warwick Business School, 2015.

35 Data from 2016: COMPSTAT, Citywide Profile 12/04/16–12/31/16, http://assets.lapdonline.org/assets/pdf/123116cityprof.pdf.

36 Ronald V. Clarke and Mike Hough, Crime and Police Effectiveness, Home Office Research Study no. 79 (London: HMSO, 1984), https://archive.org/stream/op1276605-1001/op1276605-1001_djvu.txt, as told in Tom Gash, Criminal: The Truth about Why People Do Bad Things (London: Allen Lane, 2016).

37 Kent Police Corporate Services Analysis Department, PredPol Operational Review.

38 PredPol, ‘Recent examples of crime reduction’, 2017, http://www.predpol.com/results/.

39 Aaron Shapiro, ‘Reform predictive policing’, Nature, vol. 541, no. 7638, 25 Jan. 2017, http://www.nature.com/news/reform-predictive-policing-1.21338.

40 Chicago Data Portal, Strategic Subject List, https://data.cityofchicago.org/Public-Safety/Strategic-Subject-List/4aki-r3np.

41 Jessica Saunders, Priscilla Hunt and John Hollywood, ‘Predictions put into practice: a quasi-experimental evaluation of Chicago’s predictive policing pilot’, Journal of Experimental Criminology, vol. 12, no. 3, 2016, pp. 347–71.

42 Copblock, ‘Innocent man arrested for robbery and assault, spends two months in Denver jail’, 28 April 2015, https://www.copblock.org/122644/man-arrested-for-robbery-assault-he-didnt-commit-spends-two-months-in-denver-jail/.

43 Ibid.

44 Ava Kofman, ‘How a facial recognition mismatch can ruin your life’, The Intercept, 13 Oct. 2016.

45 Ibid.

46 Copblock, ‘Denver police, “Don’t f*ck with the biggest gang in Denver” before beating man wrongfully arrested – TWICE!!’, 30 Jan. 2016, https://www.copblock.org/152823/denver-police-fck-up-again/.

47 Actually, Talley’s story is even more harrowing than the summary I’ve given here. After spending two months in jail for his initial arrest, Talley was released without charge. It was a year later – by which time he was living in a shelter – that he was arrested for a second time. This time the charges were not dropped, and the FBI testified against him. The case against him finally fell apart when the bank teller, who realized that Talley lacked the moles she’d seen on the robber’s hands as they passed over the counter, testified in court: ‘It’s not the guy who robbed me.’ He’s now suing for $10 million. For a full account, see Kofman, ‘How a facial recognition mismatch can ruin your life’.

48 Justin Huggler, ‘Facial recognition software to catch terrorists being tested at Berlin station’, Telegraph, 2 Aug. 2017, http://www.telegraph.co.uk/news/2017/08/02/facial-recognition-software-catch-terrorists-tested-berlin-station/.

49 David Kravets, ‘Driver’s license facial recognition tech leads to 4,000 New York arrests’, Ars Technica, 22 Aug. 2017, https://arstechnica.com/tech-policy/2017/08/biometrics-leads-to-thousands-of-a-ny-arrests-for-fraud-identity-theft/.

50 Ruth Mosalski, ‘The first arrest using facial recognition software has been made’, Wales Online, 2 June 2017, http://www.walesonline.co.uk/news/local-news/first-arrest-using-facial-recognition-13126934.

51 Sebastian Anthony, ‘UK police arrest man via automatic face recognition tech’, Ars Technica, 6 June 2017, https://arstechnica.com/tech-policy/2017/06/police-automatic-face-recognition.

52 David White, Richard I. Kemp, Rob Jenkins, Michael Matheson and A. Mike Burton, ‘Passport officers’ errors in face matching’, PLOSOne, 18 Aug. 2014, http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0103510#s6.

53 Teghan Lucas and Maciej Henneberg, ‘Are human faces unique? A metric approach to finding single individuals without duplicates in large samples’, Forensic Science International, vol. 257, Dec. 2015, pp. 514e1–514.e6, http://www.sciencedirect.com/science/article/pii/S0379073815003758.

54 Zaria Gorvett, ‘You are surprisingly likely to have a living doppelganger’, BBC Future, 13 July 2016, http://www.bbc.com/future/story/20160712-you-are-surprisingly-likely-to-have-a-living-doppelganger.

55 ‘Eyewitness misidentification’, The Innocence Project, https://www.innocenceproject.org/causes/eyewitness-misidentification.

56 Douglas Starr, ‘Forensics gone wrong: when DNA snares the innocent’, Science, 7 March 2016, http://www.sciencemag.org/news/2016/03/forensics-gone-wrong-when-dna-snares-innocent.

57 This doesn’t mean that false identifications are out of the question with DNA – they do happen; it just means you have a weapon on your side to make them as rare as possible.

58 Richard W. Vorder Bruegge, Individualization of People from Images (Quantico, Va., FBI Operational Technology Division, Forensic Audio, Video and Image Analysis Unit), 12 Dec. 2016, https://www.nist.gov/sites/default/files/documents/2016/12/12/vorderbruegge-face.pdf.

59 Lance Ulanoff, ‘The iPhone X can’t tell the difference between twins’, Mashable UK, 31 Oct. 2017, http://mashable.com/2017/10/31/putting-iphone-x-face-id-to-twin-test/#A87kA26aAqqQ.

60 Kif Leswing, ‘Apple says the iPhone X’s facial recognition system isn’t for kids’, Business Insider UK, 27 Sept. 2017, http://uk.businessinsider.com/apple-says-the-iphone-xs-face-id-is-less-accurate-on-kids-under-13-2017-9.

61 Andy Greenberg, ‘Watch a 10-year-old’s face unlock his mom’s iPhone X’, Wired, 14 Nov. 2017, https://www.wired.com/story/10-year-old-face-id-unlocks-mothers-iphone-x/.

62 ‘Bkav’s new mask beats Face ID in “twin way”: severity level raised, do not use Face ID in business transactions’, Bkav Corporation, 27 Nov. 2017, http://www.bkav.com/dt/top-news/-/view_content/content/103968/bkav%EF%BF%BDs-new-mask-beats-face-id-in-twin-way-severity-level-raised-do-not-use-face-id-in-business-transactions.

63 Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer and Michael Reiter, ‘Accessorize to a crime: real and stealthy attacks on state-of-the-art face recognition’, paper presented at ACM SIGSAC Conference, 2016, https://www.cs.cmu.edu/~sbhagava/papers/face-rec-ccs16.pdf.

64 Ira Kemelmacher-Shlizerman, Steven M. Seitz, Daniel Miller and Evan Brossard, The MegaFace Benchmark: 1 Million Faces for Recognition at Scale, Computer Vision Foundation, 2015, https://arxiv.org/abs/1512.00596

65 ‘Half of all American adults are in a police face recognition database, new report finds’, press release, Georgetown Law, 18 Oct. 2016, https://www.law.georgetown.edu/news/press-releases/half-of-all-american-adults-are-in-a-police-face-recognition-database-new-report-finds.cfm.

66 Josh Chin and Liza Lin, ‘China’s all-seeing surveillance state is reading its citizens’ faces’, Wall Street Journal, 6 June 2017, https://www.wsj.com/articles/the-all-seeing-surveillance-state-feared-in-the-west-is-a-reality-in-china-1498493020.

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.

Notes

A note on the title

Introduction

Power

Data

Justice

Medicine

Cars

Crime

Art

Conclusion