Law Search as Prediction

59 Pages Posted: 7 Nov 2018 Last revised: 27 Apr 2021

See all articles by Michael A. Livermore

Michael A. Livermore

University of Virginia School of Law

Faraz Dadgostari

University of Virginia

Mauricio Guim

Instituto Tecnológico Autónomo de México (ITAM) - Law School

Peter Beling

University of Virginia, Dept. of System & Information Engineering

Daniel Rockmore

Dartmouth College - Department of Mathematics; Dartmouth College - Department of Computer Science

Date Written: November 5, 2018

Abstract

The final version of this paper has been published in Artificial Intelligence and Law 23:3-34 (2021), available at https://link.springer.com/article/10.1007/s10506-020-09261-5.



The process of searching for relevant legal materials is fundamental to legal reasoning. However, despite its enormous practical and theoretical importance, law search has been given inadequate attention by scholars. In this article, we define the problem of law search, examine its normative and empirical dimensions, and investigate one particularly promising computationally based approach. We implement a model of law search based on a notion of search space and search strategies and apply that model to the corpus of U.S. Supreme Court opinions. We test the success of the model against both citation information and hand-coded legal relevance determinations.

Keywords: search, artificial intelligence and law, machine learning, topic models, legal reasoning

Suggested Citation

Livermore, Michael A. and Dadgostari, Faraz and Guim, Mauricio and Beling, Peter and Rockmore, Daniel, Law Search as Prediction (November 5, 2018). Virginia Public Law and Legal Theory Research Paper No. 2018-61, Available at SSRN: https://ssrn.com/abstract=3278398

Michael A. Livermore (Contact Author)

University of Virginia School of Law ( email )

Faraz Dadgostari

University of Virginia ( email )

1400 University Ave
Charlottesville, VA 22903
United States

Mauricio Guim

Instituto Tecnológico Autónomo de México (ITAM) - Law School ( email )

Río Hondo No.1
Álvaro Obregón, Mexico City
Mexico

Peter Beling

University of Virginia, Dept. of System & Information Engineering ( email )

1400 University Ave
Charlottesville, VA 22903
United States

Daniel Rockmore

Dartmouth College - Department of Mathematics ( email )

United States

Dartmouth College - Department of Computer Science ( email )

United States

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