Proctor : December 2017
32 PROCTOR | December 2017 Hype, heuristics and humanity As the exuberance for artificial intelligence (AI) hits fever pitch, it’s time to take a closer look at what it means for the legal world. Beyond the hype Rightly or wrongly, AI is a term commonly used to describe the performance of tasks by a machine, where such tasks were thought to require human intelligence. It is a designation which has been stretched to encompass myriad technologies, some as prosaic as a line judge in tennis. 1 “The Atlantic” recently published an article saying that, “[AI is] just a fancy name for a computer program”. 2 Yet, Stephen Hawking has said that AI “could spell the end of the human race”. 3 Which is it? It is important to appreciate the distinction between a machine that mimics the fruits of intelligence by performing specified functions, and a machine which is approximately your intellectual equal. Examples of the latter – sometimes called ‘general’ AI – and the topic of Hawking’s concern, have thus far been confined to fiction. Consider the villainous computer HAL from 2001: A Space Odyssey, and Pixar’s WALL-E . These are the kinds of awe-inspiring machines that dominate popular notions of AI. Examples of machines achieving outcomes which were once thought to be dependent on human intelligence – sometimes called ‘narrow’ AI – are wide ranging. As Richard Feynman observed, “...it is not necessary to imitate the behaviour of Nature in detail in order to engineer a device which can... surpass Nature’s abilities.”[sic.] 4 Consider IBM’s supercomputer ‘Deep Blue’ (which famously defeated Kasparov in chess in 1996),5 driverless cars, and Netflix recommending another BBC period drama because its algorithm has uncovered your guilty pleasure.6 While Justice WALL-E seems a far cry from our present reality, it’s nevertheless impressive that machines can play chess, drive cars and recommend television shows (and, for the most part, better than we can). Moreover – according to Richard and Daniel Susskind – when it comes to the evolving capabilities of machines, “there is no apparent finishing-line.” 7 What, then, are the ramifications for the legal world? AI in law Existing technologies can discern patterns, identify trends and make predictions on the basis of enormous data; automatically learn and improve from experience; recognise human language and speech (distinguishing, for example, ‘abominable’ from ‘a bomb in a ball’); and recognise and respond to human emotion.8 The potential consequences are made all the more awe-inspiring by the one- trillion-fold increase in computing power since 1956,9 the ubiquity of personal devices and the internet, and the consequential fact that “more data has been created in the past two years than in the entire previous history of the human race”. 10 Exploiting some of these advancements, law firms have adopted tools to overtake low-level cognitive aspects of the lawyer’s role, as well as augment high-level functions. Examples of the former include intelligent search engines like ROSS Intelligence, 11 algorithmic-assisted document review in eDiscovery, 12 and tools which contribute to the due diligence process by identifying and flagging anomalous contract clauses. 13 Examples of the latter include systems which analyse voluminous amounts of data to predict the outcome of litigation,14 or to generate market insights. 15 Choosing a future for AI in the law Notes 1 Ian Bogot, ‘‘Artificial Intelligence’ Has Become Meaningless’, The Atlantic (online), 4 March 2017 <https://www.theatlantic.com/technology/ archive/2017/03/what-is-artificial-intelligence/518547/>. 2 Ibid. 3 Rory Cellan-Jones, ‘Stephen Hawking warns artificial intelligence could end mankind’, The Guardian (online), 2 December 2014 <http://www.bbc.com/ news/technology-30290540>. 4 Richard Feynman, The Pleasure of Finding Things Out – The Best Short Works of Richard P. Feynman (Helix Books, 1st ed, 1999) 48. 5 Mark Anderson, ‘Twenty years on from Deep Blue vs Kasparov: how a chess match started the big data revolution’, The Conversation (online), 12 March 2017 <https://theconversation.com/twenty-years- on-from-deep-blue-vs-kasparov-how-a-chess- match-started-the-big-data-revolution-76882>. 6 Lara O’Reilly, ‘Netflix lifted the lid on how the algorithm that recommends you titles to watch actually works’, Business Insider (online), 27 February 2016, <https:// www.businessinsider.com.au/how-the-netflix- recommendation-algorithm-works-2016-2?r=US&IR=T>. 7 Daniel Susskind and Richard Susskind, The future of the Professions – How Technology Will Transform the Work of Human Experts (Oxford University Press, 1st ed, 2015) 159. 8 Ibid 275, 170. 9 Maddie Stone, ‘The Trillion Fold Increase in Computing Power, Visualised’, Gizmodo (online), 25 May 2015 <https://www.gizmodo.com.au/2015/05/the-trillion- fold-increase-in-computing-power-visualized/>. 10 Bernard Marr, ‘Big Data: 20 Mind-Boggling Facts Everyone Must Read’, Forbes (online), 30 September 2015 <https://www.forbes.com/sites/ bernardmarr/2015/09/30/big-data-20-mind-boggling- facts-everyone-must-read/#67f035e517b1>. 11 Karen Turner, ‘Meet ‘Ross,’ the newly hired legal robot’, The Washington Post (online), 16 may 2016 <https://www.washingtonpost.com/news/ innovations/wp/2016/05/16/meet-ross-the-newly- hired-legal-robot/?utm_term=.790cc5946cf1>. 12 Milan Gandhi, ‘Technology-assisted review 101 – The Rise of machines in eDiscovery’ (2017) 37(1) Proctor 6. 13 See, for example, Luminance: https://www.luminance.com/. 14 Jane Wakefield, ‘AI predicts outcome of human rights cases’, BBC (online), 23 October 2016 <http://www.bbc.com/news/technology-37727387>. 15 Misa Han, ‘‘Bloomberg terminal for lawyers’: Startup set to replace mundane legal research’, Financial Review (online), 12 December 2016 <http://www.afr.com/ business/legal/bloomberg-terminal-for-lawyers- startup-set-to-replace-mundane-legal-research- 20161204-gt3yrw>. 16 Kevin Ashley, Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age (Cambridge University Press, 1st ed, 2017) 17. 17 Ibid 18. 18 Ibid 31. 19 State v. Loomis, 881 N.W.2d 749 (Wis. 2016). 20 Adam Liptak, ‘Sent to Prison by a Software Program’s Secret Algorithms’, The New York Times (online), 1 May 2017 <https://www.nytimes.com/2017/05/01/ us/politics/sent-to-prison-by-a -software-programs- secret-algorithms.html>. 21 Unknown author, ‘State v. Loomis Wisconsin Supreme Court Requires Warning Before Use of Algorithmic Risk Assessments in Sentencing’, Harvard Law Review (online), 10 March 2017, <https:// harvardlawreview.org/2017/03/state-v -loomis/>. 22 Will Knight, ‘The Dark Secret at the Heart of AI’, MIT Technology Review (online) 11 April 2017 <https:// www.technologyreview.com/s/604087/the-dark- secret-at-the-heart-of-ai/>. 23 Hannah Devlin, ‘AI programs exhibit racial and gender biases, research reveals’, The Guardian (online) 14 April 2017 <https://www.theguardian.com/ technology/2017/apr/13/ai-programs-exhibit-racist- and-sexist-biases-research-reveals>.