Judicial Analytics and the Great Transformation of American 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

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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.

Keywords: Judicial Analytics; Causal Inference; Behavioral Judging (search for similar items in EconPapers)
Date: 2019-03
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Published in Artificial Intelligence and the Law, 2019, 27 (1), pp.15-42. ⟨10.1007/s10506-018-9237-x⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04155039

DOI: 10.1007/s10506-018-9237-x

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