Quantifying and Bounding Fairness and Unfairness in Binary and Multiclass Classification

This work proposes a new interpretable measure of unfairness that allows providing a quantitative analysis of classifier fairness, beyond a dichotomous fair/unfair distinction. It generalizes the equalized odds fairness criterion to the multiclass setting and provides methods for auditing classifiers for fairness when the confusion matrices are not available.