RankBased Multitask Learning for Fair Regression In this work, we develop a novel fairness learning approach for multitask regression models based on a biased training dataset, using a popular rankbased nonparametric independence test, i.e., Mann Whitney U statistic, for measuring the dependency between target variable and protected variables. To solve this learning problem efficiently, we first reformulate the problem as a new nonconvex optimization problem, in which a nonconvex constraint is defined based on groupwise ranking functions of individual objects. We then develop an efficient modeltraining algorithm based on the framework of nonconvex alternating direction method of multipliers (NCADMM), in which one of the main challenges is to implement an efficient projection oracle to the preceding nonconvex set defined based on ranking functions. Through the extensive experiments on both synthetic and realworld datasets, we validated the outperformance of our new approach against several stateoftheart competitive methods on several popular metrics relevant to fairness learning.

Improving Prediction Fairness via Model Ensemble Fair machine learning is a topical problem. It studies how to mitigate unethical bias against minority people in model prediction. A promising solution is ensemble learning  Nina et al 1 first argue that one can obtain a fair model by bagging a set of standard models. However, they do not present any empirical evidence or discuss effective ensemble strategy for fair learning. In this paper, we propose a new ensemble strategy for fair learning. It adopts the AdaBoost framework, but unlike AdaBoost that upweights mispredicted instances, it upweights unfairly predicted instances which we identify using a variant of Luongu0027s kNN based situation testing method 2. Through experiments on two realworld data sets, we show our proposed strategy achieves higher fairness than the bagging strategy discussed by Nina et al and several baseline methods. Our results also suggest standard ensemble strategies may not be sufficient for improving fairness.