Replace choices with independently defined suitability | AIGP
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Replace choices with independently defined suitability: Which approach is appropriate?

AIGP Understanding How to Govern AI Development Medium

Use independently defined suitability outcomes, then test performance across worker groups.

The question

A logistics company wants to train a scheduling model on historical dispatcher choices. Reviewers find that past choices systematically gave less flexible routes to one worker group, and approval requires labels representing operational suitability rather than prior preference. Which approach is appropriate?

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  1. Replace choices with independently defined suitability outcomes and test groups. ✓
    Independent outcomes align labels with operational suitability, while subgroup testing checks whether resulting performance remains uneven.
  2. Balance historical choices across groups while preserving the original target labels.
    Rebalancing frequencies does not correct the underlying meaning of labels that reflect biased preferences rather than suitability.
  3. Train directly on historical dispatcher choices.
    Unadjusted choices encode the identified allocation pattern and may teach the model to reproduce unequal treatment.
  4. Add worker-group indicators so the model can calibrate historical dispatcher preferences.
    Group indicators can preserve or formalize the biased decision rule and do not create suitability-based labels.
The trap
If past decisions reflect unequal practice, examine what the label represents before treating it as ground truth.

How to remember it

Use independently defined suitability outcomes, then test performance across worker groups.

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