Fair regression methods have the potential to mitigate societal bias concerns in health care, but there has been little work on penalized fair regression when multiple groups experience such bias. We propose a general regression framework that addresses this gap with unfairness penalties for multiple groups. Our approach is demonstrated for binary outcomes with true positive rate disparity penalties. It can be efficiently implemented through reduction to a cost-sensitive classification problem. We additionally introduce novel score functions for automatically selecting penalty weights. Our penalized fair regression methods are empirically studied in simulations, where they achieve a fairness-accuracy frontier beyond that of existing comparison methods. Finally, we apply these methods to a national multi-site primary care study of chronic kidney disease to develop a fair classifier for end-stage renal disease. There we find substantial improvements in fairness for multiple race and ethnicity groups who experience societal bias in the health care system without any appreciable loss in overall fit.

翻译：公平回归方法在医疗保健领域具有缓解社会偏见的潜力，但针对多群体存在此类偏见时的惩罚性公平回归研究尚显不足。本文提出一个通用的回归框架，通过引入针对多群体的不公平性惩罚项来填补这一空白。我们以二分类结果为例，采用真阳性率差异惩罚进行方法演示。该框架可通过转化为代价敏感分类问题实现高效计算。此外，我们提出了用于自动选择惩罚权重的新型评分函数。通过模拟实验对惩罚性公平回归方法进行实证研究，结果表明其在公平性与准确性的权衡边界上优于现有对比方法。最后，我们将这些方法应用于一项全国性多中心慢性肾脏病初级护理研究，以开发针对终末期肾病的公平分类器。研究发现，在医疗系统中遭受社会偏见的多个种族和族裔群体在公平性方面获得显著改善，且整体模型拟合度未出现明显下降。