Analysis of Vocal Implicit Bias in Scotus Decisions Through Predictive Modelling

Proceedings of Experimental Linguistics, 2018

4 Pages Posted: 9 Jan 2019

See all articles by Ramya Vunikili

Ramya Vunikili

New York University (NYU)

Hitesh Ochani

New York University (NYU)

Divisha Jaiswal

New York University (NYU)

Richa Deshmukh

New York University (NYU)

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

Elliott Ash

ETH Zürich

Date Written: December 28, 2018

Abstract

Several existing pen and paper tests to measure implicit bias have been found to have discrepancies. This could be largely due to the fact that the subjects are aware of the implicit bias tests and they consciously choose to change their answers. Hence, we’ve leveraged machine learning techniques to detect bias in the judicial context by examining the oral arguments. The adverse implications due to the presence of implicit bias in judiciary decisions could have far-reaching consequences. This study aims to check if the vocal intonations of the Justices and lawyers at the Supreme Court of the United States could act as an indicator for predicting the case outcome.

Suggested Citation

Vunikili, Ramya and Ochani, Hitesh and Jaiswal, Divisha and Deshmukh, Richa and Chen, Daniel L. and Ash, Elliott, Analysis of Vocal Implicit Bias in Scotus Decisions Through Predictive Modelling (December 28, 2018). Proceedings of Experimental Linguistics, 2018. Available at SSRN: https://ssrn.com/abstract=3307296

Ramya Vunikili

New York University (NYU) ( email )

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

Hitesh Ochani

New York University (NYU) ( email )

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

Divisha Jaiswal

New York University (NYU) ( email )

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

Richa Deshmukh

New York University (NYU) ( email )

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

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

Elliott Ash

ETH Zürich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

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