Chen, Daniel L.
(2019)
Machine Learning and the Rule of Law.
In: Law as Data : Computation, Text, and the Future of Legal Analysis
Livermore, Michael A.
and Rockmore, Daniel N.
(eds.)
Santa Fe Institute Press.
ISBN 1947864084
Preview |
Text
Download (103kB) | Preview |
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: | Book Section |
---|---|
Language: | English |
Date: | 2019 |
Subjects: | B- ECONOMIE ET FINANCE |
Divisions: | TSE-R (Toulouse) |
Site: | UT1 |
Date Deposited: | 06 Feb 2019 15:25 |
Last Modified: | 02 May 2024 13:06 |
OAI Identifier: | oai:tse-fr.eu:33352 |
URI: | https://publications.ut-capitole.fr/id/eprint/31267 |