Mahadeshwar for research assistance and D. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. It does not store any personal data.Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. The cookie is used to store the user consent for the cookies in the category 'Performance'. This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category 'Other. The cookies is used to store the user consent for the cookies in the category 'Necessary'. The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category 'Functional'. The cookie is used to store the user consent for the cookies in the category 'Analytics'. These cookies ensure basic functionalities and security features of the website, anonymously. Necessary cookies are absolutely essential for the website to function properly.

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