Artificial Intelligence in Law: Facts, Futures & Risks

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

Why are we talking about AI? 2

3

What is AI? 4

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

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

AI diagnoses diseases 7

AI translates text 8

AI advises on the law 9

AI finds cats 10

AI wins games 11

12

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

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

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

Statistical: unsupervised learning Find patterns without training 16

Statistical: supervised learning Train with labeled data 17

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

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

AI in Law 20

Watson could pass a multistate bar exam without a second thought. Robert Weber, GC IBM, 2015 21

Legal AI 22

Fastcase Bad Law Bot 23

Casetext CARA 24

Ravel judge analytics 25

Lex Machina 26

LawGeex contract analysis 27

Knowledge management 28

Advice from the B Schools 29

Expertise as a product 30

Expertise as a product line 31

Self- service guidance for clients 32

Technology & Access to Justice 33

34

The Future 35

The hype cycle 36

37

38

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, 2016 39

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

41

AI computing power 2007 2015 100,000,000,000 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 2015 42

43

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

AI & Risk Management 45

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

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