  Many applications of AI involve scoring individuals using a learned function
of their attributes. These predictive risk scores are then used to take
decisions based on whether the score exceeds a certain threshold, which may
vary depending on the context. The level of delegation granted to such systems
in critical applications like credit lending and medical diagnosis will heavily
depend on how questions of fairness can be answered. In this paper, we study
fairness for the problem of learning scoring functions from binary labeled
data, a classic learning task known as bipartite ranking. We argue that the
functional nature of the ROC curve, the gold standard measure of ranking
accuracy in this context, leads to several ways of formulating fairness
constraints. We introduce general families of fairness definitions based on the
AUC and on ROC curves, and show that our ROC-based constraints can be
instantiated such that classifiers obtained by thresholding the scoring
function satisfy classification fairness for a desired range of thresholds. We
establish generalization bounds for scoring functions learned under such
constraints, design practical learning algorithms and show the relevance our
approach with numerical experiments on real and synthetic data.
