Penalized Fair Regression for Multiple Groups in Chronic Kidney Disease
This work addresses fairness concerns in health care for multiple groups experiencing societal bias, representing an incremental advance in fair regression methods.
The authors tackled the problem of societal bias in health care by proposing a penalized fair regression framework for multiple groups, achieving substantial fairness improvements for race and ethnicity groups in chronic kidney disease without significant loss in overall fit.
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.