Epistemic Governance Requirements for Safe AI Systems

Artificial intelligence safety risks increasingly arise not from incorrect outputs, but from systems failing to recognize and signal the limits of their knowledge. Fluency and confidence in AI models often surpass epistemic reliability as they scale, posing a structural hazard when decision-critical contexts interpret these outputs as authoritative. This paper argues that such failures are best understood as governance failures rather than intelligence failures. The work reframes so-called “AI hallucinations” as factual misalignment—statistically coherent outputs produced beyond reliable epistemic boundaries—and introduces authority inversion as a key failure mode in which systems implicitly assume decision authority precisely when epistemic justification is weakest. Drawing on analogies from computing infrastructure and safety-critical systems, the paper contends that scaling models, improving accuracy, or refining ethical guidelines alone cannot resolve these risks. Instead, the paper proposes a set of system-level epistemic governance requirements, including enforced non-response states, separation of inference from authority, runtime authority boundary enforcement, and non-anthropomorphic failure classification. These requirements treat epistemic humility as an infrastructure property rather than an aspirational ethic. The paper concludes that safe AI deployment at scale requires architectures that know when to stop, defer, or escalate uncertainty—before action occurs.