In contemporary legal systems, the character of proof is being redefined with the increasing utilization of artificial intelligence in evidence proceedings. Compared to the traditional electronic records, AI-generated evidence is generated via probabilistic and algorithmic means, which questions authenticity, reliability, and epistemic trust. Nevertheless, Indian evidence law, especially the Bharatiya Sakshya Adhiniyam, 2023, is still based on the record-centric paradigm which is insufficient to tackle such issues. In this paper, I have critically analyzed the limitations of the current regime on doctrines and suggested a systematic manner in which the AI generated evidence is assessed in Indian courts. It is based on a doctrinal and comparative approach to the analysis of statutory texts, judicial practice, and the technical aspects of AI systems, as well as on the experience of the United States and the European Union. The paper has found a structural blind spot in which procedural authentication takes the place of substantive reliability, permitting possibly unreliable outputs to be accepted. The paper, to fill this gap, suggests a two-stage, algorithm-conscious admissibility framework that incorporates improved certification and systematic evaluation of reliability. It states that an algorithm-sensitive strategy is necessary in order to maintain evidentiary integrity, the right to fair trial and to align legal doctrine with technological change.
Anand et al. (Thu,) studied this question.
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