BEYOND HUMAN JUDGMENT: ARTIFICIAL INTELLIGENCE, ALGORITHMIC EVIDENCE, AND FAIR TRIAL GUARANTEES IN CRIMINAL JUSTICE

Abstract

The integration of artificial intelligence into criminal justice systems has fundamentally altered the landscape of evidence generation, procedural decision-making, and adjudication. From predictive policing algorithms and facial recognition technology to risk-assessment tools used in bail and sentencing, AI-driven mechanisms increasingly shape outcomes once reserved exclusively for human judicial reasoning. This paper critically examines the implications of such technological integration for the constitutional guarantee of a fair trial, with particular emphasis on the admissibility, reliability and contestability of algorithmically generated evidence. It interrogates whether existing evidentiary standards, developed for human testimony and conventional forensic method, are adequate to address the epistemic opacity, or black box nature, of machine-learning system, and explores procedural due process concern including the right to confront and challenge evidence, the burden of algorithmic accountability, and the risk of automation bias among judicial officer. Drawing on comparative jurisprudence from the United States, the European Union and India, the paper analyses emerging regulatory response, including the EU Artificial Intelligence Act and doctrinal development concerning explainability requirement, and argues that the unchecked deployment of AI in evidentiary and procedural context risks eroding foundational fair trial protection unless accompanied by robust frameworks for algorithmic auditability, contestability and judicial oversight. It proposes a normative framework, termed algorithmic due process, resting on mandatory disclosure of algorithmic logic, independent technical review, and judicial AI literacy, intended to reconcile technological efficiency with constitutional safeguard.