Which fairness metric measures whether a model's positive prediction rate is equal across demographic groups, regardless of actual outcomes?
Select an answer to reveal the explanation.
Short Explanation
You want the metric that just compares positive prediction rates across demographic groups, no matter what actually happened. That's demographic parity, because it only looks at how often each group gets a positive prediction.
Full Explanation
Demographic parity (statistical parity) is correct because it requires that the proportion of positive predictions be equal across all demographic groups, regardless of whether those predictions are accurate. Equalized odds is wrong because it requires equal true positive rates and equal false positive rates across groups, conditioning on actual outcomes. Calibration across groups is wrong because it requires that predicted probabilities match actual outcome rates within groups. Individual fairness is wrong because it requires that similar individuals receive similar predictions.