The Collapse of Correction: Closed Informational Loops in Safety-Critical Work

This working paper examines how closed informational loops can degrade AI systems, organizational judgment, and safety-critical work. Drawing from information theory, model-collapse research, reinforcement learning from human feedback, and operational safety practice, the paper argues that AI systems require continual grounding in fresh human judgment and external verification signals to preserve correction capacity. The paper extends the concept of recursive degradation beyond model training and applies it to expertise formation, human verification, operational drift, and AI-enabled decision-making. The central claim is that organizations risk weakening the same human and institutional mechanisms that keep AI outputs connected to operational reality. When expert judgment is extracted into AI systems while junior roles, field learning, and human verification capacity are reduced, the result is a population-level closed loop: the system consumes expertise faster than it regenerates it. The paper proposes Human, AI, and Organizational Performance (HAOP) as an operational governance frame for identifying where AI becomes a performing element inside work systems, where verification anchors are weak, and where closed informational loops may create risk in safety-critical domains.