Justice in the Age of Algorithms: Ethical and Legal Frameworks for AI in Judicial Decision-Making

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Dr. Jesmin Banu Khan; Md Rishad Khan; Sudipta Biswas

Justice in the Age of Algorithms: Ethical and Legal Frameworks for AI in Judicial Decision-Making

Authors: Dr. Jesmin Banu Khan, Md Rishad Khan, Sudipta Biswas

Unique Paper ID: 183662
Volume: 12
Issue: 3
PageNo: 2382-2385

Keywords: Artificial Intelligence Algorithmic Justice Explainability Judicial Decision-Making Legal Ethics.

Abstract:
This paper examines the ethical and legal dimensions of integrating artificial intelligence (AI) systems into judicial decision-making, focusing on ensuring fairness, accountability, transparency, and due process. Methods: The researchers conducted a multi-method study comprising (1) a systematic review of literature on AI in judicial contexts, (2) legal-normative analysis of existing frameworks across jurisdictions, and (3) a qualitative stakeholder survey of judges, legal scholars, and technologists. Results: The literature reveals concerns over algorithmic bias, opacity (“black-box” systems), and erosion of judicial discretion. Legal analyses highlight uneven regulatory approaches, with some jurisdictions adopting algorithmic-impact assessments and others lagging. Survey respondents emphasized the need for transparency mandates, human-in-the-loop oversight, and rights to explanation. Conclusions: While AI can augment judicial efficiency, without robust ethical and legal safeguards, it risks undermining the legitimacy of courts. The researchers proposes a principled framework combining (a) bias audits, (b) mandated explainability, (c) accountability measures, and (d) regulatory oversight. Future research should pilot these safeguards in real-world tribunals and evaluate their impact on fairness and public trust.

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ISSN: 2349-6002
Volume: 12
Issue: 3
PageNo: 2382-2385

Justice in the Age of Algorithms: Ethical and Legal Frameworks for AI in Judicial Decision-Making

Available:https://ijirt.org/article?manuscript=183662

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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