Annotation quality control determines whether a labeled batch is suitable for its intended use. A high agreement number alone cannot establish that the label scheme represents the task or that rare but important cases were handled correctly. Define acceptance before reviewing the delivery, then preserve the evidence behind the decision.
State what an acceptable label means
Document the unit being labeled, permitted context, required fields and label definitions. Explain missing evidence, ambiguous examples and multiple valid outcomes. For a multi-label task, make clear whether omissions are errors and whether labels may overlap. A reviewer cannot reliably assess a rule that was never specified.
Separate calibration from batch inspection
Calibration checks whether reviewers can apply the instructions consistently. Batch inspection checks the delivered data under an agreed review plan. Reusing a few familiar calibration examples is not the same as independently reviewing the batch. Keep the purpose of each set clear and protect examples whose disclosure would undermine the check.
Sample deliberately and disclose the limits
Choose a review approach that fits the decision and available expertise. Inspect important task slices and known failure boundaries as well as common examples. A targeted sample can reveal a particular error but may not estimate its prevalence across the whole batch. Record the sampling method and avoid claiming that unreviewed examples passed.
Classify errors by the correction they need
Distinguish an incorrect label from incomplete fields, unreadable source material, ambiguous guidelines or a schema mismatch. An illustrative image-labeling batch might contain accurate object classes but inconsistent boundary rules. Correcting the instructions could be more useful than asking reviewers to repeat the same judgment under the same ambiguity. No delivered project or measured gain is implied by this scenario.