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Measure class errors and capacity before cost-sensitive: Which next step best supports threshold selection?

AIGP Understanding How to Govern AI Development Hard

Measure class-specific errors and review capacity before selecting a cost-sensitive threshold.

The question

A financial document processor flags submissions for manual fraud review. Missing fraud has severe consequences, but the review team has fixed capacity. Overall accuracy is high; class-specific errors and available capacity are unknown. Which next step best supports threshold selection?

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  1. Measure class errors and capacity before cost-sensitive tuning ✓
    False-positive and false-negative rates, fraud severity, and review capacity are needed to choose a defensible cost-sensitive threshold.
  2. Lower the threshold immediately
    This may reduce missed fraud but could exceed the team’s capacity; the relevant tradeoff has not been measured.
  3. Set equal weights for missed fraud and unnecessary review
    Equal weights contradict the stated severe consequence of missed fraud unless evidence establishes equivalent costs.
  4. Maximize validation-set accuracy before changing the threshold
    Overall accuracy can conceal the asymmetric error costs and says nothing about whether review demand is feasible.
The trap
For asymmetric risks, obtain class-specific error rates and capacity constraints before changing a threshold.

How to remember it

Measure class-specific errors and review capacity before selecting a cost-sensitive threshold.

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