Machine Learning and Rule of Law

Chen, Daniel L. (2018) Machine Learning and Rule of Law. TSE Working Paper, n. 18-975, Toulouse

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Official URL: http://tse-fr.eu/pub/33149

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: Monograph (Working Paper)
Language: English
Date: December 2018
Place of Publication: Toulouse
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Institution: Université Toulouse Capitole
Site: UT1
Date Deposited: 21 Dec 2018 11:14
Last Modified: 06 Feb 2019 16:02
OAI ID: oai:tse-fr.eu:33149
URI: http://publications.ut-capitole.fr/id/eprint/28400

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