Machine Learning and the Rule of Law

Law as Data, Santa Fe Institute Press, ed. M. Livermore and D. Rockmore, 2019(16)

12 Pages Posted: 3 Jan 2019 Last revised: 20 Apr 2020

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: January 6, 2019

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 extra legal 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.

Keywords: Law, Artificial Intelligence, Machine Learning

Suggested Citation

Chen, Daniel L., Machine Learning and the Rule of Law (January 6, 2019). Law as Data, Santa Fe Institute Press, ed. M. Livermore and D. Rockmore, 2019(16). Available at SSRN: https://ssrn.com/abstract=3302507

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 )

Toulouse School of Economics
1, Esplanade de l'Université
Toulouse, 31080
France

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