How to Design Risk-Based Human Oversight for AI Systems

Human oversight is effective only when it is designed around the risk and consequence of an AI decision. This insight explains why reviewing every output does not scale and compares practical controls such as confidence thresholds, exception routing, dual approvals, segregation of duties, audit trails, and suspension mechanisms. It shows how low-risk systems can remain largely autonomous while high-impact decisions require explicit validation and traceability. Technology leaders should embed oversight into system architecture rather than add it as a final compliance step.