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Recalibrating Justice: Artificial Intelligence, Ethics, And The Evolution Of Judicial Decision- Making




Sakshi Mathur, PhD Scholar, Department of Humanities, School of Liberal Education, Galgotias University, Greater Noida, Uttar Pradesh


ABSTRACT


Technologies based on AI and machine learning are increasingly becoming part of the application of law, from predictive policing to automated analysis of cases to sentence suggestions. This study explores the intersection of computational law and judicial ethics by considering the merits and drawbacks of algorithmic decision-making in the courts. It evaluates the accuracy, transparency, and accountability of tools deploying AI to a legal reasoning process and examines the implications for a fair trial, the equality of all before the law, and the exercise of professional duty. It undertakes a mixed-methods design of a quantitative assessment of algorithmic bias with qualitative studies of structural jurisprudence and regulation, unpacking the epistemic tension between efficiency and justice in computational jurisprudence. The discovery of systemic patterns of bias and opacity demands increased ethical oversight, as well as a human-in-the-loop interventionist approach. Finally, we recommend a hybrid model of courts of assistance when engaging with computational law, in which computational tools supplement reasoning, rather than replacing it, while also ensuring progress in technology aligns with core legal values.


Keywords: computational law, artificial intelligence, judicial ethics, algorithmic bias, legal technology, fairness, automation, human oversight



Indian Journal of Law and Legal Research

Abbreviation: IJLLR

ISSN: 2582-8878

Website: www.ijllr.com

Accessibility: Open Access

License: Creative Commons 4.0

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Licensing: 

 

All research articles published in The Indian Journal of Law and Legal Research are fully open access. i.e. immediately freely available to read, download and share. Articles are published under the terms of a Creative Commons license which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

 

Disclaimer:

The opinions expressed in this publication are those of the authors. They do not purport to reflect the opinions or views of the IJLLR or its members. The designations employed in this publication and the presentation of material therein do not imply the expression of any opinion whatsoever on the part of the IJLLR.

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