Audit and improve label quality: Which mitigation best | AIGP
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Audit and improve label quality: Which mitigation best addresses the immediate evidence gap?

AIGP Understanding How to Govern AI Development Medium

Inconsistent and missing defect labels undermine evaluation; improving label validity is more immediate than adding data or model complexity.

The question

A manufacturer trains a defect classifier from inspection records, but many records lack confirmed defect labels and inspectors used inconsistent criteria. Which mitigation best addresses the immediate evidence gap?

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  1. Audit and improve label quality. ✓
    Confirmed, consistently defined labels are necessary to evaluate whether predictions correspond to actual manufacturing defects.
  2. Increase model complexity.
    Greater complexity cannot repair missing or inconsistent labels and may obscure whether apparent performance reflects reliable defect information.
  3. Report confidence intervals only.
    Statistical uncertainty estimates cannot correct systematic label inconsistency or reveal whether the measured target is valid.
  4. Add unlabeled production images.
    Additional unlabeled images may broaden exposure, but they do not resolve uncertainty about the target outcome used for evaluation.
The trap
Before trusting metrics, verify that the outcome labels are sufficiently complete, consistent, and meaningful.

How to remember it

Inconsistent and missing defect labels undermine evaluation; improving label validity is more immediate than adding data or model complexity.

How many of these would you get right?

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