Judicial Analytics and the Great Transformation of American Law

Journal of Artificial Intelligence and the Law, Forthcoming

43 Pages Posted: 7 Jan 2019

See all articles by Daniel L. Chen

Daniel L. Chen

Directeur de Recherche, Centre National de la Recherche Scientifique, Toulouse School of Economics, Institute for Advanced Study in Toulouse, University of Toulouse Capitole, Toulouse, France

Date Written: October 14, 2018

Abstract

Predictive judicial analytics holds the promise of increasing efficiency and fairness of law. Judicial analytics can assess extra-legal factors that influence decisions. Behavioral anomalies in judicial decision-making offer an intuitive understanding of feature relevance, which can then be used for debiasing the law. A conceptual distinction between inter-judge disparities in predictions and inter- judge disparities in prediction accuracy suggests another normatively relevant criterion with regards to fairness. Predictive analytics can also be used in the first step of causal inference, where the features employed in the first step are exogenous to the case. Machine learning thus offers an approach to assess bias in the law and evaluate theories about the potential consequences of legal change.

Suggested Citation

Chen, Daniel L., Judicial Analytics and the Great Transformation of American Law (October 14, 2018). Journal of Artificial Intelligence and the Law, Forthcoming. Available at SSRN: https://ssrn.com/abstract=3306071

Daniel L. Chen (Contact Author)

Directeur de Recherche, Centre National de la Recherche Scientifique, Toulouse School of Economics, Institute for Advanced Study in Toulouse, University of Toulouse Capitole, Toulouse, France ( email )

21 allée de Brienne
31015 Toulouse cedex 6 France
Toulouse, 31015
France

Register to save articles to
your library

Register

Paper statistics

Downloads
88
Abstract Views
1,596
rank
286,332
PlumX Metrics