Expert-in-the-Loop Evaluation: Closing the SME Agreement Gap

The text discusses the importance of distinguishing between Human-in-the-Loop (HITL) and Expert-in-the-Loop (EITL) methodologies in AI systems, particularly in high-stakes domains like healthcare, legal, and financial services. HITL is a runtime control mechanism where humans make decisions on specific production-agent actions, ensuring safety and compliance. In contrast, EITL focuses on the credibility of evaluation systems, using domain experts to define, calibrate, and refine metrics that grade AI output. The challenge lies in closing the agreement gap between automated judges and subject matter experts (SMEs) to ensure eval systems are reliable enough for release decisions without constant expert oversight. The text also outlines strategies for building and calibrating expert evaluation panels, emphasizing the importance of structured annotation, rubric design, and sampling strategies to maintain measurement credibility. By transforming expert feedback into automated judges, organizations can achieve scalable, trustworthy evaluations that support both real-time decisions and audit readiness.