IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04533928.html
   My bibliography  Save this paper

Analysis of vocal implicit bias in SCOTUS decisions through predictive modelling

Author

Listed:
  • 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)

  • Elliott Ash

    (ETH Zürich - Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich])

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

  • Ramya Vunikili & Hitesh Ochani & Divisha Jaiswal & Richa Deshmukh & Daniel L. Chen & Elliott Ash, 2018. "Analysis of vocal implicit bias in SCOTUS decisions through predictive modelling," Post-Print hal-04533928, HAL.
    • Handle: RePEc:hal:journl:hal-04533928
    Note: View the original document on HAL open archive server: https://hal.science/hal-04533928
    as

    Download full text from publisher

    File URL: https://hal.science/hal-04533928/document
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-04533928. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.