Analysis of vocal implicit bias in SCOTUS decisions through predictive modelling

Ramya Vunikili, Hitesh Ochani, Divisha Jaiswal, Richa Deshmukh, Daniel L. Chen () and Elliott Ash
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Ramya Vunikili: NYU - New York University [New York] - NYU - NYU System
Hitesh Ochani: NYU - New York University [New York] - NYU - NYU System
Divisha Jaiswal: NYU - New York University [New York] - NYU - NYU System
Richa Deshmukh: NYU - New York University [New York] - NYU - NYU System
Daniel L. Chen: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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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.

Date: 2018-08-28
Note: View the original document on HAL open archive server: https://hal.science/hal-04533928v1
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Published in ExLing 2018 : 9 th Tutorial and Research Workshop on Experimental Linguistics, Université Paris Diderot, Aug 2018, Paris, France

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Related works:
Working Paper: Analysis of Vocal Implicit Bias in SCOTUS Decisions Through Predictive Modelling (2018)
Working Paper: Analysis of Vocal Implicit Bias in SCOTUS Decisions Through Predictive Modelling (2018)
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