  One often finds in the literature connections between measures of fairness
and measures of feature importance employed to interpret trained classifiers.
However, there seems to be no study that compares fairness measures and feature
importance measures. In this paper we propose ways to evaluate and compare such
measures. We focus in particular on SHAP, a game-theoretic measure of feature
importance; we present results for a number of unfairness-prone datasets.
