Human-in-the-Loop
Systems design where humans remain actively involved in AI decision-making processes.
Also known as: HITL, Human oversight, Human-AI collaboration
Category: Concepts
Tags: ai, designs, safety, workflows, oversight
Explanation
Human-in-the-loop (HITL) is a design approach where humans remain actively involved in AI systems' decision-making processes - reviewing, approving, correcting, or overriding AI outputs. Instead of full automation, humans and AI collaborate with appropriate oversight. Why HITL matters: AI systems make errors (hallucinations, biases), some decisions require human judgment (ethical, contextual), accountability requires human involvement, and trust builds through verification. Implementation patterns: approval workflows (human reviews before AI action), exception handling (human handles edge cases), feedback loops (human corrections improve AI), and supervision (human monitors AI operation). When HITL is essential: high-stakes decisions (medical, legal, financial), novel situations (outside training distribution), ethical considerations (value judgments), and regulatory requirements. Tradeoffs: HITL adds latency and cost, humans can become rubber-stampers (automation bias), and scaling is limited by human capacity. Designing good HITL: make human review meaningful (not just clicking 'approve'), surface relevant information, enable easy correction, and collect feedback to improve AI. For knowledge workers, HITL thinking means: maintaining appropriate oversight, not blindly trusting AI, and designing workflows where human judgment adds value.
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