Evaluate correlated features and subgroup effects: During | AIGP
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Evaluate correlated features and subgroup effects: During review, which conclusion is decisive?

AIGP Understanding How to Govern AI Development Medium

Excluding protected fields does not remove proxy risk; assess correlations and subgroup effects.

The question

A company is procuring software to rank applicants for internal training. The vendor removed a protected-attribute field, but the model uses neighborhood and commute-distance features. During review, which conclusion is decisive?

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  1. Drop both proxy features from the model.
    Removing the features may be a possible mitigation, but it is not the required analytical conclusion; their relevance and effects must first be assessed.
  2. Evaluate correlated features and subgroup effects for disparities. ✓
    Proxy analysis should examine what the features encode and whether their use produces unequal outcomes, despite exclusion of the protected field.
  3. Review correlations between features and protected groups.
    Correlation review is useful, but correlation alone does not establish whether ranking outcomes reproduce group disparities.
  4. Approve the features after documenting that no protected attribute is stored.
    Not storing the protected field does not show that neighborhood or commute features cannot reproduce protected-group disparities.
The trap
Evaluate the information a feature carries and its observed subgroup effects, not merely its name or storage status.

How to remember it

Excluding protected fields does not remove proxy risk; assess correlations and subgroup effects.

How many of these would you get right?

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