Machine Learning and Rule of Law

Chen, Daniel L. (2019) Machine Learning and Rule of Law. Computational Analysis of Law, 27 (1). pp. 15-42.

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Official URL: http://tse-fr.eu/pub/33352

Abstract

Predictive judicial analytics holds the promise of increasing the fairness of law. Much empirical work observes inconsistencies in judicial behavior. By predicting judicial decisions—with more or less accuracy depending on judicial attributes or case characteristics—machine learning offers an approach to detecting when judges most likely to allow extralegal biases to influence their decision making. In particular, low predictive accuracy may identify cases of judicial “indifference,” where case characteristics (interacting with judicial attributes) do no strongly dispose a judge in favor of one or another outcome. In such cases, biases may hold greater sway, implicating the fairness of the legal system.

Item Type: Article
Language: English
Date: March 2019
Refereed: Yes
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 06 Feb 2019 15:25
Last Modified: 08 Oct 2019 23:06
["eprint_fieldname_oai_identifier" not defined]: oai:tse-fr.eu:33352
URI: http://publications.ut-capitole.fr/id/eprint/31267

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