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

arXiv: https://arxiv.org/abs/1911.01509 · HTML: https://mlsanity.com/paper/1911.01509/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making](https://mlsanity.com/paper/2205.04790/) (2022)
- [An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness](https://mlsanity.com/paper/2109.14653/) (2021)

## Abstract (arXiv)

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.