Understanding racial bias in health using the Medical Expenditure Panel Survey data

2019/11/04 by Moninder Singh, Singh, Moninder, Karthikeyan Natesan Ramamurthy +1
Economics, Econometrics and Finance · Health Professions · #Computers and Society (cs.CY) #Employment and Welfare Studies #FOS: Computer and information sciences #Healthcare Policy and Management #Healthcare Systems and Challenges #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.1911.01509

openalex publication_date 2019/11/04 · openalex created_date 2019/11/22 · openalex updated_date 2026/07/28

Abstract

Over the years, several studies have demonstrated that there exist significant disparities in health indicators in the United States population across various groups. Healthcare expense is used as a proxy for health in algorithms that drive healthcare systems and this exacerbates the existing bias. In this work, we focus on the presence of racial bias in health indicators in the publicly available, and nationally representative Medical Expenditure Panel Survey (MEPS) data. We show that predictive models for care management trained using this data inherit this bias. Finally, we demonstrate that this inherited bias can be reduced significantly using simple mitigation techniques.

Citations

• AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and\n Mitigating Unwanted Algorithmic Bias

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