A human-in-the-loop workflow gives a person a defined opportunity to evaluate and change an AI-supported result. It is effective only when the reviewer has the relevant evidence, sufficient time and authority to reject the recommendation.
Place review at the decision where an error would matter. Distinguish approval of a draft from authorization to send it or act on it. Define which cases always require review and which may proceed only after clear validation rules are satisfied.
Show the original input, supporting sources and material uncertainties. Avoid presenting only a polished conclusion that encourages rubber-stamping. Test whether reviewers can find realistic errors and whether escalated cases reach someone qualified to resolve them.
Monitor review workload, disagreements and missed issues. Adjust the workflow when queues grow or staff cannot perform a meaningful check. Human involvement should reduce the identified risk rather than serve as a label attached to an otherwise automatic process.
Open Sources Used
This page uses open and institutional references as a frame; the final decision still belongs to the company record, threshold and owner.
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