Human-in-the-Loop (HITL)

Human-in-the-loop, abbreviated HITL, describes an AI workflow in which people review, guide, correct, or approve part of the system’s operation. Human involvement may supply training labels, resolve uncertain cases, authorize sensitive actions, or handle exceptions that exceed automated rules. The arrangement is common in healthcare, finance, content moderation, customer support, and other settings where mistakes carry meaningful consequences. Adding a reviewer is not enough by itself: teams need clear escalation criteria, useful context, manageable workloads, and records of who decided what. Feedback should also be monitored for inconsistency or bias so human judgment improves the system rather than becoming an invisible source of error.