Machine Learning and the Rule of Law

Daniel L. Chen
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Daniel L. Chen: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CNRS - Centre National de la Recherche Scientifique

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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.

Date: 2019
Note: View the original document on HAL open archive server: https://hal.science/hal-04566341v1
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Published in Michael A. Livermore; Daniel N. Rockmore. Law as Data : Computation, Text, and the Future of Legal Analysis, 27 (1), Santa Fe Institute Press, 2019, 978-1947864085

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04566341

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