Published July 24, 2026
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The New Digital Evidence Paradigm in Criminal Justice: Artificial Intelligence, Deepfakes, Blockchain Authentication, and Predictive Policing.
Authors/Creators
- 1. Independent Researcher in International Relations Gurugram, Haryana, India.
- 2. Threat Intelligence Analyst Nfilade Security Solutions, Gurugram, Haryana, India.
Description
Preservation copy of an article published in International Journal of Law, Politics and Governance. Read the full article: https://ioro.org/ijlpg/article/144705701640/144705701640.
The increasing integration of artificial intelligence (AI), blockchain technologies, and big data analytics into criminal justice systems is transforming the collection, authentication, analysis, and evaluation of digital evidence. While these innovations enhance investigative capabilities through automated data processing, forensic analysis, facial recognition, and predictive analytics, they also create significant challenges for evidentiary reliability. In particular, the proliferation of AI-generated content and increasingly sophisticated deepfakes has complicated the authentication of digital evidence, while predictive policing systems have raised concerns regarding algorithmic bias, transparency, accountability, and due process. Although existing scholarship has examined these technologies individually, relatively little research has explored their combined implications for digital evidence and criminal justice decision-making within a unified analytical framework. This research paper evaluates the implications of AI-generated content, blockchain authentication systems, and predictive policing technologies for the reliability, admissibility, and legitimacy of digital evidence in contemporary criminal justice systems.
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© 2026 The Author(s). Published by IORO Publications. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original author and source are credited, a link to the license is provided, and any changes are indicated.
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Identifiers
- DOI
- 10.64823/ijlpg.2601007
- ISSN
- 3143-7028
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- https://ioro.org/ijlpg/article/144705701640/144705701640 (URL)
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