The research article “Dissecting racial bias in an algorithm used to manage the health of populations” by Ziad Obermeyer, Brian Powers, Christine Vogeli and Sendhil Mullainathan has been well received by science and media. It was published in the journal Science on 25 October 2019. From the abstract: “Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. 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.” (Abstract) The authors suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts. The journal Nature quotes Milena Gianfrancesco, an epidemiologist at the University of California, San Francisco, with the following words: “We need a better way of actually assessing the health of the patients.”
BY OLIVER BENDEL
