P R E S E N T A T I O N T I T L E

Artificial Intelligence in Law:Facts, Futures & Risks

Michael Mills

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Why are we talking about AI?

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What is AI?

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

Anon.

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“Artificial intelligence is the science and engineering of making intelligent machines.”

John McCarthy

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A I d iagnoses d i seases

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A I t rans lates tex t

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A I adv i ses on the law

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A I f inds cats

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A I w ins games

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I t ’ s a l l about in fe rence

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

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In fe rence 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

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Log ica l in fe rence

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, THENE&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

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S tat i s t ica l : unsuperv i sed learn ing

Find patterns without training

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S tat i s t ica l : superv i sed learn ing

Train with labeled data

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S tat i s t ica l : re in fo rcement learn ing

Self-train with rewards over time

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Why now?

AI as a Service

The Cloud

Compute Power Big Data

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AI in Law

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“Watson could pass a multistate bar exam without a second thought.”

Robert Weber, GC IBM, 2015

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Legal A I

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Fas tcase Bad Law Bot

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Casetext CARA

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Rave l – judge analy t ics

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Lex Machina

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LawGeex cont ract ana lys i s

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Knowledge management

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Adv ice f rom the B Schoo l s

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Exper t i se as a product

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Exper t i se as a product l ine

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Se l f - se rv ice gu idance fo r c l ient s

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Technology & Access to Justice

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The Future

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The hype cyc le

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“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

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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

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A I comput ing power 2007 – 2015

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10,000,000,000

20,000,000,000

30,000,000,000

40,000,000,000

50,000,000,000

60,000,000,000

70,000,000,000

80,000,000,000

90,000,000,000

100,000,000,000

CPU 2007 GPU 2008 CPU Cloud 2011 GPU Cloud 2015

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

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AI & Risk Management

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Ques t ions …

• What methods do you use now to evaluate & assure the quality of human processes & work product?

• How will you evaluate & assure the quality of AI-augmented 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?

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More ques t ions …

• 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 AI-improved processes?

• What impact will they have on pricing and billing models?• What impact will they have on performance

measurement and compensation models?