IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-03629734.html
   My bibliography  Save this paper

The Promise of Machine Learning for the Courts of India

Author

Listed:
  • Sandeep Bhupatiraju
  • 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)

Abstract

Artificial Intelligence (‘AI') and machine learning (‘ML') — adaptive computer programs that attempt to perform functions typically associated with the human mind — offer new opportunities for improving the decision-making capacity and productivity of the Indian judiciary. First, the algorithmic analysis of legal data can provide human decision-makers with timely alerts of biases at critical decision-making moments, and also propose real-time corrections for these behaviors. Analysis of texts for patterns of bias and discrimination, for example, can augment the capabilities of judges and lawyers and systematise processes of review. Second, machine learning tools can also be deployed to clean, systematize, and standardize legal data. Though the judiciary has made significant investments in data systems, the variations in quality across states and administrative boundaries prevent a deeper analysis of the data. Third, the deployment of machine learning methods creates new opportunities to ensure procedural fairness and also enables legal scholars to better study the courts themselves. When cases are randomly assigned to judges researchers can evaluate the impact of judicial decisions — since judges in this scenario do not choose their cases and end up with them randomly, observed rulings reflect their deliberations in the case rather than the process of justice that led them to be assigned the case. We emphasize however, that technology must be viewed as a complement to human decision-makers and not a substitute. Only technologies that aid humans, rather than replace them, are suitable in this setting.

Suggested Citation

  • Sandeep Bhupatiraju & Daniel L. Chen, 2021. "The Promise of Machine Learning for the Courts of India," Post-Print hal-03629734, HAL.
  • Handle: RePEc:hal:journl:hal-03629734
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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-03629734. 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.