Human in the Loop
Human in the loop is a planned review step. A named person gets the facts, power, and duty to act. They can change, approve, reject, or stop AI-assisted work.
Another example
A clinical coding specialist sees an AI code and chart evidence. The screen also shows warnings and a four-hour review goal. Before billing, the specialist can approve, edit, or reject the code. The choice is logged. A tired reviewer who only clicks Approve does not give real oversight.
Main parts
- 01review trigger
- 02authorized reviewer
- 03recorded decision
Use it when
Use this term when a risky result needs an accountable person's judgment, review, or approval first.
Do not use it when
Review is not meaningful when people lack facts, authority, staff, or time. They also need a way to record feedback.
Name used in code
AI proposal → accountable review → recorded decision Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate Human in the Loop to make AI behavior measurable and tied to evidence. User context: A clinical coding specialist sees an AI code and chart evidence. The screen also shows warnings and a four-hour review goal. Before billing, the specialist can approve, edit, or reject the code. The choice is logged. A tired reviewer who only clicks Approve does not give real oversight. AI method or concept: Human in the Loop. Why it fits: Use this term when a risky result needs an accountable person's judgment, review, or approval first. Do not use it when: Review is not meaningful when people lack facts, authority, staff, or time. They also need a way to record feedback. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): It is official for that source. Other platforms or teams may use the term in another way. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of Human in the Loop. Definition: Human in the loop is a planned review step. A named person gets the facts, power, and duty to act. They can change, approve, reject, or stop AI-assisted work. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.