Also known as: human in the loop (HITL) Β· HITL Β· Human in the Loop
HITL, or human-in-the-loop, is an approach in which people remain involved in shaping, reviewing, approving, or improving AI system outputs or decisions. It is used to combine machine efficiency with human judgment, especially in higher-risk or context-sensitive tasks. HITL may include oversight, feedback, validation, escalation, or model training activities.
Human in the loop refers to the involvement of human oversight and decision-making in the processes that involve AI and automated systems. This approach allows for critical decisions, especially those impacting individuals, to be reviewed, verified, and influenced by human judgement and expertise.
A loosely-defined idiom that could mean either of the following: - A policy of viewing generative AI output critically or skeptically. - A strategy or system for ensuring that people help shape, evaluate, and refine a model's behavior. Keeping a human in the loop enables an AI to benefit from both machine intelligence and human intelligence. For example, a system in which an AI generates code which software engineers then review is a human-in-the-loop system.