Inspectable Learning Governance A Forensic Architecture for Safe AI Systems

Contemporary AI systems increasingly incorporate adaptive or learning components, yet most provide limited or no means for independent verification of how, when, or under whose authority learning occurs. This opacity complicates oversight, accountability, and trust—particularly in safety-critical, regulatory, or research contexts. This paper presents an architectural approach to inspectable learning governance: a system in which learning-relevant state changes—whether deliberate, authorized updates or detected unintended adaptations—are constrained, recorded, cryptographically anchored, and optionally human-attested. The architecture emphasizes metadata-only logging, immutable integrity chains, external verification, and explicit human responsibility, without reliance on access to model internals. Rather than making claims about intelligence, alignment, or correctness, the proposed design focuses on enabling post-hoc inspection and evidentiary review of learning activity. Safety and accountability are treated not as assumed properties of the system, but as matters that can be evaluated through verifiable records.