Epistemic Hidden Defects in Artificial Intelligence: Classical ʿIlal al-Ḥadīth as a Framework for AI Verification

Abstract

This working paper examines whether the classical methodology of ʿilal al-Ḥadīth—the detection of hidden defects in apparently sound reports—can provide a useful conceptual framework for evaluating artificial-intelligence outputs in specialist knowledge environments. It develops the concept of the “epistemic hidden defect”: a weakness in provenance, attribution, inference, context, evidential hierarchy, or scholarly status that may remain concealed beneath a fluent and apparently authoritative AI-generated response. Drawing on classical Hadith-critical methodology and contemporary AI research, the paper distinguishes computational capability from scholarly authority. It develops the concepts of the Authority Ceiling, Digital Isnād, competence-specific human oversight, and the No-Autonomous-Grading Principle as safeguards for AI-assisted scholarship. The paper argues for an AI-assisted rather than AI-autonomous model of specialist research, in which computational systems widen the evidential field while consequential scholarly judgement remains traceable, reviewable, and accountable.