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Compare labels with independently assessed service needs: What should reviewers examine first?

AIGP Understanding How to Govern AI Development Hard

Compare historical labels with independent service-need assessments to test whether decisions represent the intended outcome.

The question

A customer-service model learns from historical escalation decisions. Supervisors escalated some customers more often because of inconsistent staffing practices, and demographic fields were removed before training. Approval requires evidence that the target reflects service need rather than past decisions. What should reviewers examine first?

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  1. Compare escalation rates across customer segments
    Segment rate comparisons can reveal disparities, but without an independent need standard they cannot determine whether labels were justified.
  2. Measure accuracy against historical escalation labels
    Historical-label accuracy can reward reproduction of staffing-driven decisions without showing that escalations reflected actual service needs.
  3. Remove additional customer attributes from training
    Removing attributes may reduce direct use of sensitive fields, but cannot correct biased labels already encoding inconsistent escalation practices.
  4. Compare labels with independently assessed service needs
    Independent service-need assessments test whether historical escalation labels reflect the intended outcome instead of inconsistent supervisory decisions.
The trap
If labels are past human decisions, ask what independent evidence shows those decisions represented the intended outcome.

How to remember it

Compare historical labels with independent service-need assessments to test whether decisions represent the intended outcome.

How many of these would you get right?

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