Compare line-specific errors: What conclusion is most | AIGP
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Compare line-specific errors: What conclusion is most justified?

AIGP Understanding How to Govern AI Development Easy

High aggregate accuracy can conceal materially different error rates; evaluate performance separately for relevant production lines.

The question

A manufacturing defect detector achieves 97% overall accuracy. Its error rate is 2% for one production line and 14% for another, despite similar test-set sizes. What conclusion is most justified?

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  1. Replace accuracy with a single alternative metric for all production lines
    Changing metrics without line-specific analysis may conceal rather than clarify the observed performance difference.
  2. Approve deployment based on aggregate accuracy and document the line difference
    Documentation alone does not resolve a substantial subgroup disparity hidden by the aggregate metric.
  3. Compare line-specific errors ✓
    The differing line-specific error rates show aggregate accuracy masks subgroup performance and require disaggregated evaluation.
  4. Collect a larger combined test set before examining production-line results
    The existing line-specific difference is already informative; combining more data could continue masking the disparity.
The trap
Whenever subgroup results differ, do not let a favorable pooled metric substitute for subgroup analysis.

How to remember it

High aggregate accuracy can conceal materially different error rates; evaluate performance separately for relevant production lines.

How many of these would you get right?

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