FROC: Building Fair ROC from a Trained Classifier
FROC: Building Fair ROC from a Trained Classifier
This paper considers the problem of fair probabilistic binary classification with binary protected groups. The classifier assigns scores, and a practitioner predicts labels using a certain cut-off threshold based on the desired trade-off between false positives vs. false negatives. It derives these thresholds from the ROC of the classifier. The …