Dissecting racial bias in an algorithm used to manage the health of populations
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- Ziad Obermeyer
- School of Public Health, University of California, Berkeley, Berkeley, CA, USA.
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- Brian Powers
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA.
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- Christine Vogeli
- Mongan Institute Health Policy Center, Massachusetts General Hospital, Boston, MA, USA.
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- Sendhil Mullainathan
- Booth School of Business, University of Chicago, Chicago, IL, USA.
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<jats:title>Racial bias in health algorithms</jats:title> <jats:p> The U.S. health care system uses commercial algorithms to guide health decisions. Obermeyer <jats:italic>et al.</jats:italic> find evidence of racial bias in one widely used algorithm, such that Black patients assigned the same level of risk by the algorithm are sicker than White patients (see the Perspective by Benjamin). The authors estimated that this racial bias reduces the number of Black patients identified for extra care by more than half. Bias occurs because the algorithm uses health costs as a proxy for health needs. Less money is spent on Black patients who have the same level of need, and the algorithm thus falsely concludes that Black patients are healthier than equally sick White patients. Reformulating the algorithm so that it no longer uses costs as a proxy for needs eliminates the racial bias in predicting who needs extra care. </jats:p> <jats:p> <jats:italic>Science</jats:italic> , this issue p. <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" issue="6464" page="447" related-article-type="in-this-issue" vol="366" xlink:href="10.1126/science.aax2342">447</jats:related-article> ; see also p. <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" issue="6464" page="421" related-article-type="in-this-issue" vol="366" xlink:href="10.1126/science.aaz3873">421</jats:related-article> </jats:p>
収録刊行物
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- Science
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Science 366 (6464), 447-453, 2019-10-25
American Association for the Advancement of Science (AAAS)
