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Article . 2026
License: CC BY
Data sources: Datacite
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
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THE ADMISSIBILITY OF AI-GENERATED EVIDENCE IN CRIMINAL PROCEEDINGS: A COMPARATIVE ANALYTICAL STUDY IN LIGHT OF QATARI LEGISLATION

Authors: Nagi Saleh Alyami;

THE ADMISSIBILITY OF AI-GENERATED EVIDENCE IN CRIMINAL PROCEEDINGS: A COMPARATIVE ANALYTICAL STUDY IN LIGHT OF QATARI LEGISLATION

Abstract

This study examines the conceptual framework of automatically generated evidence, encompassing its technical and legal characteristics. It analyses the scope of its admissibility within the Qatari judiciary while benchmarking its efficacy against European legal precedents. Furthermore, it elucidates the procedural safeguards essential for protecting the rights of the accused. The research addresses a precise legal problem: the absence of explicit statutory provisions regulating the acceptance or rejection of AI-generated evidence, and the subsequent challenges posed to the integrity of criminal justice. The findings indicate that while Qatari law recognises electronic evidence in principle, it lacks specific provisions for evidence generated by Artificial Intelligence (AI). Consequently, the determination of its evidentiary weight remains subject to judicial discretion, leading to inconsistent rulings. Conversely, the comparative analysis highlights that the European Union has adopted a rigorous approach, classifying AI systems utilised within the judiciary as "high-risk." The study concludes by recommending an amendment to the Qatari Criminal Proceedings Law or the Cybercrime Prevention Law to incorporate a dedicated chapter on automatically generated evidence. It further suggests the development of a procedural manual to document the chain of custody for such evidence based on a clear and objective classification.

Related Organizations
Keywords

Criminal Evidence, Intelligent Evidence, Legal Probative Value, Criminal Proceedings, Qatari Legislation.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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