Metrics Driven Human Oversight Framework for AI Systems

The deployment of AI systems in healthcare demands continuous, risk-aligned oversight to ensure safe and responsible operations. We propose a Metrics-driven model that calibrates human involvement based on metrics risk (including accuracy, precision, recall, F1-score, transparency etc). High-risk Systems require Human-in-Command (HIC) oversight, with final decision authority. Medium-risk systems operate under Human-in-the-Loop (HITL) models, with human supervision and active feedback. Low-risk systems function under Human-on-the-Loop (HOTL) oversight, where humans monitor system outputs and intervene only when anomalies occur. This metrics driven Human Oversight Framework for AI Systems balances innovation with accountability. Unlike existing AI governance approaches that treat performance metrics and human oversight as separate considerations, this work explicitly links metrics-derived risk thresholds to proportional human oversight models (HIC, HITL, HOTL), providing an auditable and operational framework for regulated environments.