Natural Language Processing for Lawyers and Judges

12 Pages Posted: 9 Jun 2020 Last revised: 18 May 2021

See all articles by Frank Fagan

Frank Fagan

South Texas College of Law Houston; EDHEC Augmented Law Institute

Date Written: May 13, 2020

Abstract

This Essay reviews Michael Livermore and Daniel Rockmore’s edited collection, Law as Data. It discusses each of the chapters, spends some time addressing the differences between predictive and causal inferences for law — an important theme that runs throughout the book — and then turns to a discussion of how natural language processing can help describe legal rules. Contemporary studies of black-letter law which populate today’s treatises and law reviews often rely on cases that have been carefully selected by jurists. As a consequence, distilled statements of law suffer from selection bias regardless of a jurist’s best efforts. Natural language processing, which can describe legal doctrine by examining thousands of cases at once, can help reduce that bias. It can increase confidence in long-standing rules, uncover hidden rationales for their application, and clarify that some matters, such as those embodied in good legal standards, remain best unresolved.

Keywords: Natural Language Processing, Law, Predictive Inference, Causal Inference, Lawyering, Judging

Suggested Citation

Fagan, Frank, Natural Language Processing for Lawyers and Judges (May 13, 2020). Michigan Law Review, Vol. 119, No. 6, p. 1399, 2021, Available at SSRN: https://ssrn.com/abstract=3564966 or http://dx.doi.org/10.2139/ssrn.3564966

Frank Fagan (Contact Author)

South Texas College of Law Houston

1303 San Jacinto Street
Houston, TX 77002
United States

EDHEC Augmented Law Institute

Roubaix, 59057
France

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