Is Justice Really Blind? And Is It Also Deaf?

Chen, Daniel L., Kumar, Manoj, Motwani, Vishal and Yeres, Philip (2019) Is Justice Really Blind? And Is It Also Deaf? Computational Analysis of Law. (In Press)

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Official URL: http://tse-fr.eu/pub/32429

Abstract

Using data from 1946–2014, we show that audio features of lawyers’ introductory statements and lawyers’ facial attributes improve the performance of the best prediction models of Supreme Court outcomes. We infer face attributes using the MIT-CBCL human-labeled face database and infer voice attributes using a 15-year sample of human-labeled Supreme Court advocate voices. We find that image features improved prediction of case outcomes from 63.8% to 65.6%, audio features improved prediction of case outcomes from 66.8% to 68.8%, image and audio features together improved prediction of case outcomes from 66.9% to 67.7%, and the weights on lawyer traits are approximately half the weight of the most important feature from the models without image or audio features. Predictions of Justice votes with image and/or audio features however remained more similar relative to their baselines. We interpret this difference to suggest that human biases are more relevant in close cases.

Item Type: Article
Language: English
Date: 2019
Refereed: Yes
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 22 May 2018 08:56
Last Modified: 24 Jan 2019 00:17
OAI ID: oai:tse-fr.eu:32429
URI: http://publications.ut-capitole.fr/id/eprint/25831

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