Fairness for AUC via Feature Augmentation

Understand

We study fairness in the context of classification where the performance is measured by the area under the curve (AUC) of the receiver operating characteristic.

• AUC is commonly used to measure the performance of prediction models.
• The same classifier can have significantly varying AUCs for different protected groups and, in real-world applications, it is often desirable to reduce such cross-group differences.
• We address the problem of how to acquire additional features to most greatly improve AUC for the disadvantaged group.