Human-in-the-Loop (HITL)
An AI design pattern where human oversight and decision-making are integrated into the AI system's workflow, ensuring humans can review, approve, or override AI recommendations before action is taken.
Human-in-the-loop is required by the EU AI Act for high-risk AI applications in healthcare, law enforcement, and employment. The approach is standard in medical AI (radiologists review AI findings), autonomous vehicles (safety drivers), and content moderation (human reviewers for edge cases). HITL reduces error rates — AI-assisted radiologists detect 20% more cancers than either AI or humans alone. However, automation bias can undermine HITL effectiveness: studies show humans tend to defer to AI recommendations even when they should not. Designing effective HITL systems requires careful attention to when and how humans are prompted to intervene.
Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
AI Safety
The interdisciplinary field focused on preventing AI systems from causing harm, encompassing alignment, robustness, interpretability, and governance of AI technologies.
Deepfake
AI-generated synthetic media — images, video, or audio — that realistically depict events or statements that never occurred, created using deep learning techniques.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information, presenting it with the same confidence as accurate responses.
Prompt Engineering
The practice of crafting and optimizing input prompts to elicit desired outputs from AI language models, emerging as both a skill and a job category in the AI economy.
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