Machine Learning and Rule of Law

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.

Replaced by

Daniel L. Chen, “Machine Learning and the Rule of Law”, 2019in Law as Data: Computation, Text, and the Future of Legal Analysis, Michael Livermore, and Daniel Rockmore (eds.), Santa Fe Institute Press, 2019.

Reference

Daniel L. Chen, “Machine Learning and Rule of Law”, IAST Working Paper, n. 18-88, December 2018.