Retraining may reinforce under-investigation because: What risk | AIGP
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Retraining may reinforce under-investigation because: What risk arises specifically from the label-generation

AIGP Understanding How to Govern AI Development Easy

Historical allocation affects the labels, allowing retraining to reproduce and reinforce under-investigation.

The question

A public-benefit agency retrains a prioritization model using past investigation outcomes. Staff historically investigated fewer cases from one neighborhood, so those outcomes reflect unequal attention rather than a neutral measure of need. The agency proposes checking only aggregate accuracy. What risk arises specifically from the label-generation process?

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  1. Retraining may reinforce under-investigation because historical outcomes encode unequal attention.
    When labels reflect which cases staff previously investigated, the model can learn and repeat that allocation pattern rather than predict underlying need.
  2. The agency may need additional representative testing before deployment.
    Contextual testing is appropriate, but this option does not state how historically selective labels can reproduce under-investigation.
  3. The agency may need to document the historical data source before retraining.
    Documentation is useful, but it does not explain how selective investigation can become a repeated allocation pattern in the model.
  4. Aggregate accuracy can conceal different error rates across neighborhoods.
    This is a valid evaluation limitation, but it does not identify the risk created by training on outcomes shaped by unequal investigation attention.
The trap
Ask how labels were generated; past decisions may encode unequal attention rather than underlying need.

How to remember it

Historical allocation affects the labels, allowing retraining to reproduce and reinforce under-investigation.

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