Critical Perspectives on Algorithmic Bias and Due Process in Criminal Adjudication

Abstract

The integration of artificial intelligence and algorithmic risk-assessment into criminal adjudication marks a shift from judicial discretion to data-driven governance. Proponents claim these tools eliminate bias and docket delays; however, this review demonstrates that algorithmic models instead institutionalize historical inequities under a veneer of scientific infallibility. By ingesting datasets corrupted by over-policing, machine-learning systems mistake enforcement patterns for objective behavioral forecasts, penalizing marginalized groups through proxy variables and feedback loops. Crucially, this automated turn conflicts with due process and equal protection. When private vendors assert trade-secret privileges to shield source code and feature weights, defendants are denied the right to confront adverse evidence and secure reasoned rulings. Furthermore, automation bias leads judges to defer uncritically to probabilistic classifications, delegating qualitative judicial authority to corporate actors. Synthesizing comparative frameworks across the United States, European Union, and India, this paper argues for constitutional supremacy. It proposes enforceable safeguards, including codified algorithmic discovery rights, mandatory human-in-the-loop limits, pre-deployment impact assessments, and strict exclusionary rules, to ensure technological modernization does not compromise human liberty.