Judicial Analytics and the Great Transformation of American Law

Chen, Daniel L. (2019) Judicial Analytics and the Great Transformation of American Law. Artificial Intelligence and the Law, 27 (1). pp. 15-42.

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Official URL: http://tse-fr.eu/pub/33345

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 interjudge 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.

Item Type: Article
Language: English
Date: March 2019
Refereed: Yes
Uncontrolled Keywords: Judicial Analytics, Causal Inference, Behavioral Judging
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 06 Feb 2019 15:09
Last Modified: 08 Oct 2019 23:06
["eprint_fieldname_oai_identifier" not defined]: oai:tse-fr.eu:33345
URI: http://publications.ut-capitole.fr/id/eprint/31263

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