Statistical Parity
One of the fairness criteria in machine learning, requiring that a model's rate of positive predictions be approximately equal across protected groups. Its mathematical definition is simple, but it ignores differences in individual qualification and may lead to reverse discrimination.
Timeline (last 90 days)
Pre-deployment fairness assessment should include three dimensions: statistical parity analysis across demographic groups, equal opportunity assessment for qualified candidates, and intersectional bias assessment.
Statistical parity requires the model to have roughly the same proportion of positive predictions across different protected groups, and its blind spot is ignoring differences in individual qualifications, so forcibly equalizing may cause reverse discrimination.
Statistical parity and equal opportunity often cannot be satisfied simultaneously in mathematical terms, and actual deployment requires choosing a primary metric based on the business scenario and documenting the reasons for the trade-off.
All Facts (3)
Pre-deployment fairness assessment should include three dimensions: statistical parity analysis across demographic groups, equal opportunity assessment for qualified candidates, and intersectional bias assessment.
50%UnverifiedStatistical parity requires the model to have roughly the same proportion of positive predictions across different protected groups, and its blind spot is ignoring differences in individual qualifications, so forcibly equalizing may cause reverse discrimination.
50%UnverifiedStatistical parity and equal opportunity often cannot be satisfied simultaneously in mathematical terms, and actual deployment requires choosing a primary metric based on the business scenario and documenting the reasons for the trade-off.
50%