CEGA Affiliate Ziad Obermeyer discusses how health care algorithms can prioritize wealthier, white patients, exacerbating the racial gap in medical care.
“In recent weeks, alarming statistics have emerged detailing the disproportionate impact of COVID-19 on black communities, Latinx communities and other marginalized groups in the United States.
In today’s Berkeley Conversations: COVID-19 event, Jennifer Chayes, associate provost of the Division of Computing, Data Science, and Society and dean of the School of Information, spoke with three UC Berkeley experts about how relying on data and algorithms to guide pandemic response may actually serve to perpetuate these inequities — and what researchers and data scientists can do to reverse the patterns.
Algorithms are only as good as the input we give them. Ziad Obermeyer, acting associate professor of health policy and management at Berkeley, learned this lesson when he and his collaborators dug into a commonly-used algorithm used to decide who gets access to health care management services in hospitals.”
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