Bhupatiraju, Sandeep and Chen, Daniel L. (2021) The Process of Machine Learning for the Courts of India. National Law School of India Review, vol.33 (n°2).

Full text not available from this repository.


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.

Item Type: Article
Language: English
Date: 2021
Refereed: Yes
Place of Publication: Bangalore
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 13 Sep 2022 14:15
Last Modified: 08 Jun 2023 07:13
OAI Identifier:
View Item