ALGORITHMIC EVIDENCE ON TRIAL: EVALUATING THE ADMISSIBILITY OF AI-GENERATED FORENSIC OUTPUTS UNDER DIVERGENT GLOBAL JUDICIAL STANDARDS

AUTHOR – DINESH KUMAR B* & MS. HEMAVATHY**

* STUDENT AT SCHOOL OF EXCELLENCE IN LAW, THE TAMILNADU DR.AMBEDKAR LAW UNIVERSITY

** PROFESSOR AT SCHOOL OF EXCELLENCE IN LAW, TNDALU

BEST CITATION – DINESH KUMAR B & MS. HEMAVATHY, ALGORITHMIC EVIDENCE ON TRIAL: EVALUATING THE ADMISSIBILITY OF AI-GENERATED FORENSIC OUTPUTS UNDER DIVERGENT GLOBAL JUDICIAL STANDARDS, INDIAN JOURNAL OF LEGAL REVIEW (IJLR), 6 (4) OF 2026, PG. 87-98, APIS – 3920 – 0001 & ISSN – 2583-2344.

Abstract

The advent of artificial intelligence (AI) in forensic science has revolutionized evidence generation, from facial recognition to digital trace analysis and predictive modelling. Yet, AI-generated forensic outputs face unprecedented scrutiny in courtrooms worldwide due to divergent judicial standards governing admissibility. This research evaluates the challenges of introducing algorithmic evidence under frameworks such as the U.S. Daubert standard, which demands testability, peer review, and known error rates, contrasted with the United Kingdom’s more flexible common law approach and the inquisitorial models of civil law jurisdictions across the European Union. Central tensions arise from AI’s ‘black box’ opacity, where proprietary algorithms obscure reasoning, raising concerns over reproducibility and bias. Empirical analysis of landmark rulings reveals rejection rates exceeding 40% for unvalidated AI tools in adversarial proceedings, underscoring risks to judicial integrity. Key barriers include insufficient validation benchmarks, the absence of forensic-specific error metrics, cross-jurisdictional data privacy conflicts, and judicial unfamiliarity with AI limitations such as dataset skews that amplify racial biases. Proposed reforms advocate hybrid standards: mandatory AI explainability audits, international certification extending ISO 17025 frameworks, Rule 707-style disclosures for machine-generated evidence, and federated learning for privacy-preserving cross-border validation. By dissecting admissibility criteria through comparative legal lenses, this study charts pathways for harmonised protocols, ensuring AI enhances rather than erodes forensic trustworthiness. Balancing innovation with due process demands urgent, evidence-based judicial evolution.

Keywords: algorithmic evidence, AI admissibility, Daubert standard, forensic AI, judicial standards, black box opacity, evidentiary reliability, algorithmic bias, explainable AI, cross-jurisdictional forensics.