Error and stockout rates stratified by store size | AIGP
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Error and stockout rates stratified by store size: Which evidence is most useful?

AIGP Understanding the Foundations of AI Governance Hard

Stratified outcome evidence can reveal group-level harms hidden by pooled forecasting performance.

The question

A retailer’s demand-planning model performs well overall, but small stores report recurring stockouts while large stores receive excess inventory. Leadership wants evidence before approval that addresses individual and group effects. Which evidence is most useful?

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  1. Error and stockout rates stratified by store size and region. ✓
    Stratified operational outcomes reveal whether harms concentrate across groups while preserving the business context of shortages and excess inventory.
  2. A feature-importance chart for the forecasting model.
    Feature importance may describe model behavior, but it does not establish unequal operational outcomes among stores.
  3. Interviews with planners who approve replenishment recommendations.
    Planner perspectives may identify workflow concerns, but interviews alone cannot quantify concentrated stockouts or excess allocation.
  4. One combined forecast error across all stores.
    A pooled metric can conceal systematic differences between store groups and cannot identify whether particular locations experience recurring harm.
The trap
If harm may cluster, disaggregate outcomes by the affected operational groups.

How to remember it

Stratified outcome evidence can reveal group-level harms hidden by pooled forecasting performance.

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