HPM Seminar Series

Algorithmic Bias

Ziad Obermeyer, MD
Associate Professor
Health Policy & Management
UC Berkeley, School of Public Health

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.
Remedying this disparity would increase the percentage of Black patients receiving additional
help from 17.7 to 46.5%. 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. Thus, despite health care cost appearing to be an effective proxy for health
by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of
convenient, seemingly effective proxies for ground truth can be an important source of algorithmic
bias in many contexts.

Unfortunately, we are unable to live stream this seminar.

Light refreshments served. Everyone is invited to attend this seminar.

Building access instructions for non-University guests
Display this event page on your smart phone at the Mayo Building public entrance on Church Street, or the Moos Tower public entrance on Washington Avenue.