Human-in-the-Loop

A design requirement where a qualified educator retains meaningful intervention and override capability for system outputs that affect learning or assessment, ensuring that automated recommendations do not replace critical human judgment in pedagogical or evaluative contexts.

A human oversight role in which a person is actively involved in the AI system's decision process and can directly take actions, approve, modify or halt system outputs during operation or specific lifecycle stages. It denotes hands-on, operational human intervention as part of system control and governance.

A governance and operational control approach where a human provides explicit oversight, validation or retains final decision-making authority over outputs or actions of an AI system, including defined escalation paths and limits on automated decision-making for high-impact cases.

A control approach requiring human oversight, review, or intervention for AI outputs deemed high-risk, ensuring that final decisions impacting individuals or critical services involve accountable human decision-makers. The Guidelines mandate human-in-the-loop controls for high-impact domains and high-risk outputs to mitigate harms from hallucinations or erroneous automated actions.

An oversight model in which a human reviewer must intervene, authorize, or review AI-generated outputs before they result in enforceable outcomes or life-safety actions; the bill mandates this model (or human-on-the-loop alternatives) for systems with potentially enforceable or safety-critical outputs and requires escalation procedures and defined human roles.

A design and operational requirement ensuring that qualified human operators retain meaningful oversight, review, intervention capability or final decision authority over high‑risk automated functions to prevent, detect or mitigate harms.

Human-in-the-loop denotes mechanisms ensuring meaningful human oversight and decision-making at critical stages of AI use, including review and approval gates to prevent fully automated high-impact decisions and to validate AI outputs for accuracy and appropriateness.

Human-in-the-loop is an approach mandated by the guidance, ensuring that human judgment remains central to decision-making processes. It emphasizes that AI systems should support, rather than replace, human expertise and final accountability.

A requirement for human intervention or oversight in the decision-making process of an automated system to ensure accountability and prevent errors.

A governance requirement ensuring that significant administrative decisions are not made solely by machines without the possibility of human review or intervention.

A model of interaction where an AI system assists a human, but the human retains final control and responsibility for the decision.

A socio-technical system design where AI acts as an assistive tool and human operators maintain control and responsibility for final decisions.

A human oversight arrangement where a person is actively involved in the AI system’s decision‑making cycle (including review, correction, or approval), encompassing related modalities such as human‑on‑the‑loop and human‑in‑control to ensure appropriate oversight.

A governance practice where a teacher or school staff reviews, moderates, or validates AI-generated outputs prior to dissemination to learners, ensuring pedagogical alignment and preventing harms. The guide emphasizes this as a core mitigation for higher‑risk uses like assessment.