On The Potential Of AI For Evidence Evaluation

Abstract

<div> This paper explores the potential of artificial intelligence (AI) systems in the evaluation of forensic evidence, a subject of growing interest and debate. While concerns persist around the opacity and lack of explainability in so-called "black-box" models, we argue that these characteristics alone should not preclude their use in court. Drawing a parallel with scent identification by trained dogs-an opaque yet accepted forensic science method-we suggest that rigorous validation is the more appropriate requirement. We clarify common misconceptions, particularly regarding the repeatability of AI outputs, and propose both a priori and a posteriori validation strategies. Crucially, we emphasize the role of expert oversight in human-in-the-loop workflows, ensuring that AI is used as a tool within the expert's domain of responsibility. When properly validated and applied by trained practitioners, black-box AI can contribute meaningfully and reliably to evidence evaluation. </div>

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