The Use of Artificial Intelligence by Big Law PDF
Preview The Use of Artificial Intelligence by Big Law
The Use of Artificial Intelligence by Big Law Association of Corporate Counsel - Chicago February 27, 2018 Presented by: Daniel Martin Katz, Jay Leib, Hal Marcus, Geoffrey Vance Perkins CoieLLP Where we’ve learned The experience with AI in legal so far Hal Marcus The promise Legal started nearly a decade ago to leverage machine learning for automatic conceptual analysis and predictive coding (aka TAR). OpenText Confidential. ©2017 All Rights Reserved. 2 The critical driver 70% 70% 98% of the cost of of discovery of all US all US litigation goes cost goes to attorney federal lawsuits settle, to discovery review of documents making discovery the real trial in most cases OpenText Confidential. ©2017 All Rights Reserved. 3 The climate Technological competency is an ethical and professional obligation ABA Model Rule 1.1, Comment 8 “To maintain the requisite knowledge and skill, a lawyer should keep abreast of changes in the law and its practice, including the benefits and risks associated with relevant technology, engage in continuing study and education and comply with all continuing legal education requirements to which the lawyer is subject.” OpenText Confidential. ©2017 All Rights Reserved. 4 The climate Discovery efforts need only be reasonable and proportional to the case Amended Federal Rule of Civil Procedure 26(b)(1) “Scope in General. …Parties may obtain discovery regarding any nonprivileged matter that is relevant to any party's claim or defense and proportional to the needs of the case…” (emphasis added). OpenText Confidential. ©2017 All Rights Reserved. 5 The unwieldy origin One of many actual Boolean search strings from tobacco litigation (((master settlement agreement OR msa) AND NOT (medical savings account OR metropolitan standard area)) OR s. 1415 OR (ets AND NOT educational testing service) OR (liggett AND NOT sharon a. liggett) OR atco OR lorillard OR (pmi AND NOT presidential management intern) OR pm usa OR rjr OR (b&w AND NOT photo∗ OR phillip morris OR batco OR ftc test method OR star scientific OR vector group OR joe camel OR (marlboro AND NOT upper marlboro)) AND NOT (tobacco∗ OR cigarette∗ OR smoking OR tar OR nicotine OR smokeless OR synar amendment OR philip morris OR r.j. reynolds OR (“brown and williamson”) OR (“brown & williamson”) OR bat industries OR liggett group) OpenText Confidential. ©2017 All Rights Reserved. 6 The tech Unsupervised machine learning China US trade relations pLSA Analytics Engine relations Disney economic Beijing intellectual property negotiations human China? How probable is it that terms like rights free “China“or “trade“might occur? imports US Additional index terms can be added automatically via statistical inference! OpenText Confidential. ©2017 All Rights Reserved. 7 The tech Unsupervised machine learning Documents Terms pLSA Analytics Engine economic imports TRADE trading Latent Concepts Concept expression probabilities are “Unmixing” of superimposed Conclusion: ⇒ No prior knowledge estimated based on all documents that concepts is achieved by statistical about concepts required, context and are dealing with a concept. learning algorithm. term co-occurrences are exploited OpenText Confidential. ©2017 All Rights Reserved. 8 The tech “Java” Unsupervised machine learning pLSA Analytics Engine java indonesia software sumatra coffee development island starbucks script espresso java java bean beach code latte bali compile CONCEPT 34 CONCEPT 76 CONCEPT 149 OpenText Confidential. ©2017 All Rights Reserved. 9
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