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From Code To Court: A Comprehensive Analysis Of AI Generated Evidence In The Criminal Justice System


Avani Tungare, Symbiosis Law School, Pune


ABSTRACT


The law of evidence has undergone a radical change with the introduction of Artificial Intelligence (AI) into the criminal court system, presenting both benefits and challenges. AI has completely changed the way that evidence is gathered and examined. Examples of these technologies include automated document processing, facial recognition, and predictive policing. This study examines the definition of artificial intelligence (AI) and the standards used to identify evidence as AI-generated. It looks at how AI is now used in criminal justice, emphasizing how it affects accountability and the problems that come with it. The paper also discusses the objections raised against AI's application in law enforcement, in particular the worries over potential hazards and ingrained prejudices.


The paper also examines the requirements for AI-generated evidence's admissibility, evaluating how current legal systems either support or need these technological developments. The goal of the project is to improve the validity and equity of AI-based evidence in court processes by assessing the benefits and limitations of AI-driven evidence and suggesting innovative procedural protections. The article aims to further the current conversation on striking a balance between innovation and justice in a changing legal environment by providing this thorough analysis.


Keywords: Evidence, Artificial Intelligence, admissibility, criminal justice



Commenti


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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​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.

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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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