Question

To satisfy the condition of equalized odds in algorithmic bias auditing, what must be true about the relationship between the model&#x27;s predictions, the actual labels, and the protected demographic groups?

Accepted Answer

Equalized odds is a fairness criterion requiring that a model performs with the same level of accuracy across different protected demographic groups. To satisfy this condition, the model&#x27;s predictions must be statistically independent of the protected group attribute, conditional on the actual label. This means the model must have identical true positive rates and identical false positive rates for every group. The true positive rate is the proportion of actual positive cases that the model correctly identifies as positive, while the false positive rate is the proportion of actual negative cases that the model incorrectly identifies as positive. For example, if a hiring algorithm is used, equalized odds requires that qualified candidates from all demographic groups have the same probability of being selected, and unqualified candidates from all groups have the same probability of being incorrectly selected. When these two rates are balanced across all demographics, the model does not disproportionately favor or penalize any specific group based on its predictive errors.

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To satisfy the condition of equalized odds in algorithmic bias auditing, what must be true about the relationship between the model's predictions, the actual labels, and the protected demographic groups?