Verification Overrun: The Safety Hazard Inside AI Oversight

Version 2.1 — Editorial correction, July 2026. This version corrects the enumeration of Verification Capacity inputs in the Variable #2 passage to match the assessment method (domain knowledge, evidence and context, time, authority). The definition of Verification Overrun and the underlying two-variable construct are unchanged from Version 2. Concept DOI: 10.5281/zenodo.20874288 (resolves to the latest version). Superseded by Version 2 (July 2026), which retitles the hazard Verification Overrun and revises the definition; the concept DOI resolves to the current version. V1 description (original, June 20, 2026): This working paper introduces Cognitive Overrun as a safety-critical failure mode in AI oversight. Cognitive Overrun occurs when a worker remains formally responsible for reviewing or approving AI-supported output, but the rate, density, or ambiguity of that output exceeds the worker’s capacity to verify what matters. The paper argues that AI does not always reduce cognitive load. In many work systems, it relocates cognitive demand from producing work to supervising, checking, correcting, and answering for machine-generated output. When human verification is treated as the primary control, but the human lacks sufficient time, context, authority, workload capacity, or access to the underlying evidence, oversight can become the appearance of control without the function of control. Drawing on recent research on AI-related cognitive fatigue, AI-enabled work intensification, critical-thinking reduction, automation complacency, and the ironies of automation, the paper frames Cognitive Overrun as a work-system design problem rather than an individual attention failure. It applies this concept to safety-critical and high-consequence settings where humans are asked to supervise AI systems that generate, filter, route, recommend, or act faster than human verification can reasonably occur. The paper situates Cognitive Overrun within the Human, AI, and Organizational Performance (HAOP) framework, which treats work systems as shaped by three interacting performers: the human, the AI, and the organization. It concludes that human attention must be treated as a finite safety-critical resource and explicitly accounted for in workload models, risk assessments, job design, and safety management systems.Originally published as a LinkedIn article on June 20, 2026. This Zenodo version includes minor editorial formatting corrections and two added explanatory paragraphs clarifying voluntary work intensification and the contrast between cognitive load and Cognitive Overrun.