Artificial Intelligence in Law: Facts, Futures & Risks

Transcription

1 Artificial Intelligence in Law: Facts, Futures & Risks Michael Mills PRESENTATION TITLE

2 Why are we talking about AI? 2

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4 What is AI? 4

5 Artificial intelligence is the study of how to make real computers act like the ones in the movies. Anon. 5

6 Artificial intelligence is the science and engineering of making intelligent machines. John McCarthy 6

7 AI diagnoses diseases 7

8 AI translates text 8

9 AI advises on the law 9

10 AI finds cats 10

11 AI wins games 11

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13 It s all about inference IF some fact or pattern of facts is observed with some degree of accuracy and certainty THEN we can say some other things are X or Y true / false permitted / prohibited prudent / risky relevant / not about A / about B with some degree of confidence AND we can explain why with some degree of clarity 13

14 Inference methods Logical inference human experts, heuristics, explicit representation in rules & algorithms, deterministic, transparent Statistical inference big data, machine learning (unsupervised, supervised, reinforcement), probabilistic, opaque Hybrid combine logical + statistical 14

15 Logical inference IF Company State of Organization = Delaware AND Entity Type = Corporation, THEN Applicable Law = Delaware Corporation Law IF Company Headquarters = California, THEN Applicable Law = California Corporations Code IF Applicable Law = DE-CL and Board of Directors < 6, THEN E&O Policy Risk Level = Medium IF Applicable Law = CA-CC, THEN E&O Policy Risk Level = High IF No Company Subsidiary Organized in Kansas, THEN Kansas Tax Law = N/A 15

16 Statistical: unsupervised learning Find patterns without training 16

17 Statistical: supervised learning Train with labeled data 17

18 Statistical: reinforcement learning Self-train with rewards over time 18

19 Why now? Compute Power Big Data The Cloud AI as a Service 19

20 AI in Law 20

21 Watson could pass a multistate bar exam without a second thought. Robert Weber, GC IBM,

22 Legal AI 22

23 Fastcase Bad Law Bot 23

24 Casetext CARA 24

25 Ravel judge analytics 25

26 Lex Machina 26

27 LawGeex contract analysis 27

28 Knowledge management 28

29 Advice from the B Schools 29

30 Expertise as a product 30

31 Expertise as a product line 31

32 Self- service guidance for clients 32

33 Technology & Access to Justice 33

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35 The Future 35

36 The hype cycle 36

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39 If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future. Andrew Ng,

40 What to do next? Understand the tools Don t think about AI find problems to be solved Analyze ROI though I is uncertain & R speculative Start with the simple & obvious Be prepared to fail fail fast, move on 40

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42 AI computing power ,000,000,000 90,000,000,000 80,000,000,000 70,000,000,000 60,000,000,000 50,000,000,000 40,000,000,000 30,000,000,000 20,000,000,000 10,000,000,000 0 CPU 2007 GPU 2008 CPU Cloud 2011 GPU Cloud

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44 We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten. 44

45 AI & Risk Management 45

46 Questions What methods do you use now to evaluate & assure the quality of human processes & work product? How will you evaluate & assure the quality of AIaugmented processes & work product? Like humans, technology makes mistakes. More than is so with humans, with technology the mistakes are measurable, predictable, and transparent. What level of error is acceptable? Who decides? Client or firm? Case-by-case? Firm standard? What is the standard of care? What must/should you disclose to clients about the firm s use of AI? 46

47 More questions What must/should you disclose to the firm s professional liability insurers? When a technology makes mistakes that fall below the standard of care, who is liable? Must the firm insure specifically against this sort of liability? Does disclosure to and consent of the client suffice? Can a technology provider be deemed engaged in the practice of law? How will you account for the costs & benefits of AIimproved processes? What impact will they have on pricing and billing models? What impact will they have on performance measurement and compensation models? 47