Residual linkage and inference risk remains despite | AIGP
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Residual linkage and inference risk remains despite: Which remaining concern is most specific before

AIGP Understanding the Foundations of AI Governance Hard

Removing names does not eliminate linkage or inference risk from retained and inferred attributes.

The question

A retailer’s internal data-use committee is considering loyalty-card data for demand forecasting. The proposed mitigation removes names but retains store, purchase timing, household-size estimates, and promotion-response history. Which remaining concern is most specific before authorization?

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  1. Additional historical purchases should be collected to improve forecast coverage
    More data need not improve fitness and could increase exposure or reinforce unsuitable patterns for the forecasting purpose.
  2. Residual linkage and inference risk remains despite removing names ✓
    Store patterns, timing, household estimates, and promotion history can enable linkage or reveal sensitive behavior; removing names does not eliminate that risk.
  3. The data-use record should specify retention and deletion controls
    Retention controls may be needed, but this option does not identify the specific linkage and inference concern created by the retained attributes.
  4. Forecast performance should be monitored against later demand outcomes
    Accuracy monitoring is an appropriate control, but it does not address whether the retained and inferred attributes are suitable for the approved use.
The trap
Assess retained and inferred attributes, not just names; de-identification does not automatically establish appropriate use.

How to remember it

Removing names does not eliminate linkage or inference risk from retained and inferred attributes.

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