Analysis of vocal implicit bias in SCOTUS decisions through predictive modelling
DOI:
https://doi.org/10.36505/ExLing-2018/09/0029/000362Keywords:
Speech analysis, Implicit gender bias, Machine learning, SCOTUS, FAVEAbstract
Several existing pen-and-paper tests designed to measure implicit bias have been found to contain discrepancies. This could be largely due to the fact that subjects are aware they are being tested and consciously choose to alter their answers. To address this limitation, we have leveraged machine learning techniques to detect bias in the judicial context by examining oral arguments. Because the adverse implications of implicit bias in judicial decisions can have far-reaching consequences, this study aims to determine whether the vocal intonations of Justices and lawyers at the Supreme Court of the United States can serve as a reliable indicator for predicting case outcomes.
References
Chen, D.L., Halberstam, Y., & Yu, A.C. 2016. *Covering: Mutable Characteristics and Perceptions of Voice in the US Supreme Court* (TSE Working Paper No. 16-180). Toulouse: Toulouse School of Economics.
Klofstad, C.A., Anderson, R.C., & Peters, S. 2012. Sounds like a winner: Voice pitch influences perception of leadership capacity in both men and women. *Proceedings of the Royal Society B: Biological Sciences*, 279(1738), 2698–2704.
Tigue, C.C., Borak, D.J., O’Connor, J.J.M., Schandl, C., & Feinberg, D.R. 2012. Voice pitch influences voting behavior. *Evolution and Human Behavior*, 33(3), 210–216.
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Copyright (c) 2018 Ramya Vunikili, Hitesh Ochani, Divisha Jaiswal, Richa Deshmukh, Daniel L. Chen, Elliott Ash (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles are published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
