Human-in-the-Loop
HITLAlso called: HITL, Human-in-the-Loop AI
A collaborative approach in which humans actively participate in an AI system's operation by reviewing, approving, correcting, or guiding its decisions or actions, improving accuracy, safety, accountability, and overall system performance.
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Related terms
A workflow in which one or more human reviewers must approve, modify, or reject an AI system's decisions or actions before execution, ensuring oversight, quality control, compliance, or risk mitigation.
Human OversightThe practice of monitoring, reviewing, and governing an AI system's decisions, actions, and outputs by human operators to ensure safety, accuracy, compliance, accountability, and appropriate intervention when necessary.
EscalationThe process of transferring a task, decision, or interaction from an AI system to a human or another specialized agent when predefined conditions, limitations, or risks are encountered.
Human FeedbackInformation, evaluations, corrections, or preferences provided by human users or reviewers about an AI system's outputs or behavior, which can be used to improve model performance, refine prompts, optimize workflows, or guide future decisions.
Expert ReviewAn evaluation method in which subject matter experts assess the quality, accuracy, safety, or effectiveness of an AI system, model, or output using their domain knowledge and established criteria.