AI-Generated Evidence and the Indian Criminal Process: Challenges of Reliability and Admissibility
DOI:
https://doi.org/10.5281/zenodo.23233479Trefwoorden:
Digital Forensics, Admissibility, Deepfakes, Bharatiya Sakshya Adhiniyam 2023, Electronic Evidence, Artificial IntelligenceSamenvatting
Artificial intelligence has transformed the evidentiary environment of criminal justice. Audio, video, images, documents and text can now be generated or altered with a degree of realism that makes ordinary visual or auditory inspection increasingly unreliable. India’s new evidence framework, the Bharatiya Sakshya Adhiniyam, 2023 (BSA), recognises electronic and digital records while imposing specific safeguards concerning their source, integrity, certification and hash values. The Supreme Court’s decision in Pune Bar Association v Union of India (2026) has upheld the constitutional validity of Section 63(4) and expressly recognised the heightened risks created by artificial intelligence and deepfakes. This article argues that admissibility, authenticity, reliability and probative weight must nevertheless remain analytically distinct. A technically compliant certificate can establish important aspects of provenance and integrity without establishing that an AI-generated representation is truthful, historically accurate or attributable to the person against whom it is tendered. The article examines synthetic media, AI enhancement, voice cloning, generated documents, algorithmic identification and machine-assisted forensic analysis. It analyses Sections 39, 61, 62 and 63 of the BSA, the relationship between the new regime and the Supreme Court’s Section 65B jurisprudence, and the fair-trial implications of opaque or insufficiently testable AI processes. It proposes a five-stage AI Evidence Reliability and Admissibility Test based on provenance, integrity, methodological reliability, contextual attribution and independent corroboration. The article concludes that Indian courts should neither exclude digital evidence merely because AI was involved nor presume that technical authenticity establishes truth. A calibrated, process-sensitive approach is required to preserve both technological utility and the presumption of innocence.
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