Clinical predictive models that include race as a predictor have the potential to exacerbate disparities in healthcare. Such models can be respecified to exclude race or optimized to reduce racial bias. We investigated the impact of such respecifications in a predictive model - UTICalc - which was designed to reduce catheterizations in young children with suspected urinary tract infections. To reduce racial bias, race was removed from the UTICalc logistic regression model and replaced with two new features. We compared the two versions of UTICalc using fairness and predictive performance metrics to understand the effects on racial bias. In addition, we derived three new models for UTICalc to specifically improve racial fairness. Our results show that, as predicted by previously described impossibility results, fairness cannot be simultaneously improved on all fairness metrics, and model respecification may improve racial fairness but decrease overall predictive performance.

The authors have declared no competing interest.

Research reported in this publication was supported by the National Institutes of Health under award number T15 LM007059 from the National Library of Medicine and under award number UL1 TR001857 from the National Center for Advancing Translational Sciences. It was also supported by a School of Computing and Information Predoctoral Fellowship to JWA.

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The IRB of University of Pittsburgh gave ethical approval for this work.

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