Audit group-level impacts alongside individual accuracy | AIGP
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Audit group-level impacts alongside individual accuracy: Which control best addresses the primary governance

AIGP Understanding the Foundations of AI Governance Hard

Governance must assess group harms even when individual predictions appear accurate.

The question

An insurer’s claims model usually predicts individual claim costs accurately, but it systematically assigns higher costs to one regional group, affecting pricing and service capacity. Individual reviews show few obvious errors. All other prerequisites are satisfied. Which control best addresses the primary governance concern?

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  1. Audit group-level impacts alongside individual accuracy. ✓
    Accurate individual predictions can coexist with group harm, so governance should examine disparate aggregate effects as well as individual performance.
  2. Provide case-level explanations for affected customers.
    Individual explanations may aid transparency, but they do not directly measure or mitigate the systematic regional disparity.
  3. Remove regional information from the model inputs.
    Removing an explicit region field may not remove correlated signals and could impede analysis of the reported group impact.
  4. Keep individual accuracy as the primary acceptance metric.
    Individual accuracy does not reveal systematic group-level burdens affecting pricing or service capacity.
The trap
Check both individual and group effects; good case-level performance can coexist with systematic collective harm.

How to remember it

Governance must assess group harms even when individual predictions appear accurate.

How many of these would you get right?

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