We introduce a new family of techniques to post-process (“wrap") a black-box classifier in order to reduce its bias. Our technique builds on the recent analysis of improper loss functions whose optimization can correct any twist in prediction, unfairness being treated as a twist. In the post-processing, we learn a wrapper function which we define as an α-tree, which modifies the prediction. We provide two generic boosting algorithms to learn α-trees. We show that our modification has appealing p…