Is Justice Really Blind? And is it Also Deaf?

18 Pages Posted: 2 Aug 2016

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

Manoj Kumar

New York University (NYU)

Vishal Motwani

New York University (NYU)

Phil Yeres

New York University (NYU)

Date Written: July 31, 2016

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.

Suggested Citation

Chen, Daniel L. and Kumar, Manoj and Motwani, Vishal and Yeres, Phil, Is Justice Really Blind? And is it Also Deaf? (July 31, 2016). Available at SSRN: https://ssrn.com/abstract=2816567 or http://dx.doi.org/10.2139/ssrn.2816567

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

Manoj Kumar

New York University (NYU) ( email )

Bobst Library, E-resource Acquisitions
20 Cooper Square 3rd Floor
New York, NY 10003-711
United States

Vishal Motwani

New York University (NYU) ( email )

Bobst Library, E-resource Acquisitions
20 Cooper Square 3rd Floor
New York, NY 10003-711
United States

Phil Yeres

New York University (NYU) ( email )

Bobst Library, E-resource Acquisitions
20 Cooper Square 3rd Floor
New York, NY 10003-711
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
61
Abstract Views
684
Rank
473,158
PlumX Metrics