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Article Volume 8 Issue 5 620 - 633 October 2, 2025

Integrating Cognitive Machines into Judicial Processes: A Frontier in Legal Intelligence

Lead author · Corresponding
Srinivas M.K.
Research Scholar (Law) at Department of Studies in Law, University of Mysore, India
View PDF Full text DOIhttps://doij.org/10.10000/IJLMH.1110807
Abstract

The rapid evolution of artificial intelligence (AI) technologies presents a transformative opportunity for judicial systems worldwide. Integrating Cognitive Machines into Judicial Processes: A Frontier in Legal Intelligence explores the emerging interface between law and machine intelligence, examining how AI-powered tools—particularly cognitive machines—can augment legal decision-making, enhance administrative efficiency, and broaden access to justice. This paper provides an in-depth analysis of technologies such as data mining, natural language processing (NLP), machine learning, and big data analytics, emphasizing their roles in legal research, case prediction, and automated translation. It also investigates the ongoing digital transformation within the Indian judicial system, highlighting initiatives such as e-filing, the Inter-Operable Criminal Justice System (ICJS), NSTEP for process service, and the National Judicial Data Grid (NJDG). The study underscores how mobile legal applications and natural language-based advisory systems are enabling more inclusive and responsive legal services. Ultimately, the integration of cognitive technologies into judicial workflows signals a paradigm shift toward more intelligent, efficient, and citizen-centric legal systems.

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Article
Information
International Journal of Law Management and Humanities, Volume 8, Issue 5, Page 620 - 633
DOI: https://doij.org/10.10000/IJLMH.1110807
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CC BY-NC 4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting, and building upon the work for non-commercial use, provided the original work is properly cited.
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Copyright © IJLMH 2026
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The views and opinions expressed in this manuscript are those of the author(s) alone and do not reflect the views, policies, or position of the Journal.

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