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When automation produces hundreds of records, human review may cover only a few examples. Selecting convenient, problem-free cases can leave important errors invisible. Review design should state which decision the automation supports and the consequences of an incorrect output.
Scope of the decision
A sample can include common situations together with ambiguous, borderline and failed-input examples. Targeted selection, however, does not provide a statistical error rate for all records. Define reviewer competence, comparison sources and a route for stopping use of problematic outputs in advance.
Worked review
For example, automation categorises monitoring notes. Initial checks cover only short notes and miss misclassification of longer texts containing several issues. The team adds long, conflicting and incomplete notes to review, assessing whether important concerns disappear when the system forces them into one category.
Practical checks
- Connect selection to intended use and the effect of a wrong decision.
- Include appropriate examples with ambiguity, missing information and multiple topics.
- Explain how similar affected outputs will be investigated after an error is found.
Acceptance and limits
Acceptance requires adequate review evidence for the defined use and explicit limitations. A few correct examples do not approve every output. Reassess whether the same selection approach still detects important errors when system behaviour or input characteristics change.
Frequently asked questions
- Does human approval make automation reliable?
- Not alone. What the review checks and which evidence supports it are essential.
- Must every output be recreated manually?
- Different uses require different scope. Decision risk and appropriate authorised assessment determine the depth of review.