Computer Models for Legal Prediction

Jurimetrics, Vol. 46, p. 309, 2006

U. of Pittsburgh Legal Studies Research Paper

44 Pages Posted: 14 Apr 2022

See all articles by Kevin Ashley

Kevin Ashley

University of Pittsburgh - School of Law

Stefanie Bruninghaus

University of Pittsburgh - School of Law

Date Written: 2006

Abstract

Computerized algorithms for predicting the outcomes of legal problems can extract and present information from particular databases of cases to guide the legal analysis of new problems. They can have practical value despite the limitations that make reliance on predictions risky for other real-world purposes such as estimating settlement values. An algorithm's ability to generate reasonable legal arguments also is important. In this article, computerized prediction algorithms are compared not only in terms of accuracy, but also in terms of their ability to explain predictions and to integrate predictions and arguments. Our approach, the Issue-Based Prediction algorithm, is a program that tests hypotheses about how issues in a new case will be decided. It attempts to explain away counterexamples inconsistent with a hypothesis, while apprising users of the counterexamples and making explanatory arguments based on them.

Keywords: legal analysis, AI, artificial intelligence, computerized prediction algorithms, hypothesis testing, legal arguments, case analysis

Suggested Citation

Ashley, Kevin and Bruninghaus, Stefanie, Computer Models for Legal Prediction (2006). Jurimetrics, Vol. 46, p. 309, 2006, U. of Pittsburgh Legal Studies Research Paper, Available at SSRN: https://ssrn.com/abstract=4082465

Kevin Ashley (Contact Author)

University of Pittsburgh - School of Law ( email )

3900 Forbes Ave.
Pittsburgh, PA 15260
United States

Stefanie Bruninghaus

University of Pittsburgh - School of Law

3900 Forbes Ave.
Pittsburgh, PA 15260
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
13
Abstract Views
128
PlumX Metrics