Most fair regression algorithms mitigate bias towards sensitive sub populations and therefore improve fairness at group level. In this paper, we investigate the impact of such implementation of fair regression on the individual. More precisely, we assess the evolution of continuous predictions from an unconstrained to a fair algorithm by comparing results from baseline algorithms with fair regression algorithms for the same data points. Based on our findings, we propose a set of post-processing algorithms to improve the utility of the existing fair regression approaches.

翻译：最公平的回归算法可以减轻对敏感子人群的偏向,从而改善群体一级的公平性。 在本文中,我们调查了公平回归对个人的影响。更准确地说,我们通过比较基线算法的结果和同一数据点的公平回归算法,评估从不受限制的连续预测演变为公平的算法。根据我们的调查结果,我们提出了一套后处理算法,以提高现有公平回归方法的效用。