Algorithmic Evidence in Criminal Trials: Admissibility, Explainability, and Fair-Trial Guarantees

This article argues that the lawful and ethical use of algorithmic evidence in criminal adjudication depends on a single organising principle: contestability. Algorithmic outputs should be admitted only where there is (i) task-matched empirical validation, (ii) sufficient transparency to enable adversarial testing, and (iii) clear limits on the scope of use. Part I classifies the principal families of tools—facial recognition, probabilistic genotyping, risk assessment instruments, and AIassisted digital forensics—and explains why validation must be matched to data quality, operating thresholds, and realistically encountered conditions. Part II contrasts jurisdictional approaches to reliability. In the United States, Daubert and the 2023 amendments to Rule 702 require both foundational validity and reliability-as-applied, which in turn necessitates version histories, errorrate and calibration evidence, and reproducibility. In Europe, Article 6 ECHR focuses on overall fairness, while the GDPR and the Law Enforcement Directive structure the provenance and accountability of automated processing that later enters the evidentiary record; these instruments mandate transparency and proportionality without imposing categorical source-code disclosure. Part III examines transparency and explainability. Article 22 GDPR provides protection against solely automated decisions and ensures avenues for human review, but it does not confer an absolute right to source code (Nuredin, 2023b). Meaningful contestation can nonetheless be secured through substitutes: task-matched audits; preservation of case-specific artefacts (logs, parameters, thresholds); protective-order access for defence experts; and, where feasible, interpretable models. The same section links these safeguards to due-process concerns arising from subgroup disparities in tools such as COMPAS and from feedback-loop risks in predictive policing systems. Part IV sets out implementable pathways: mandatory pre-deployment and periodic audits; safeguarded defendant access to code or functionally equivalent disclosures; expert-witness standards requiring reproducible reports, explicit error and calibration claims, and bias analysis (Nuredin, 2024); and procurement clauses that encode documentation, logging, sandbox access, and sunset triggers. The article concludes that a system in which no defendant is adjudged on the basis of an untestable algorithm—and in which audits, documentation, and calibrated judicial instructions precede courtroom use—is both normatively required and institutionally feasible.