DEI Special Seminar: Algorithmic bias and data platforms (Ziad Obermeyer)

Nov. 28 DBMI Special Seminar: Toward Diversity, Equity, and Inclusion in Informatics, Health Care, and Society

Presenter: Ziad Obermeyer, Blue Cross of California Distinguished Associate Professor of Health Policy and Management, California-Berkeley

Title: Algorithmic bias and data platforms

Abstract: We’re increasingly aware of the many ways that algorithms can encode and scale up racial bias. When designed with careful attention to label choice, algorithms can also be used to counter biases present in the health care system and ingrained in medical knowledge. To do so effectively, researchers and product developers must have access to platforms on which they can access health data for the benefit of patients and society.

Bio: Ziad trained as an emergency doctor – and he still gets away as often as he can, to a hospital in rural Arizona, to work in the ER. But these days, he spends most of his time on research and teaching at Berkeley. Inspired by his clinical practice, he builds machine learning algorithms that help doctors make better decisions. He also studies where algorithms can go wrong, and how to fix them: his work on algorithmic bias has been highly influential both in public debate about algorithms, and in regulatory oversight and civil investigations. He is a Chan Zuckerberg Biohub Investigator, a Faculty Research Fellow at the National Bureau of Economic Research, and has been named an emerging leader by the National Academy of Medicine. His work has won numerous awards, and appeared in a wide range of journals (Science, Nature Medicine, the New England Journal of Medicine, leading computer science conferences). He is a co-founder of Nightingale Open Science, a non-profit that makes massive new medical imaging datasets available for research, and Dandelion, a platform for AI innovation in health. Before coming to Berkeley, he was an Assistant Professor at Harvard Medical School and a consultant at McKinsey & Co.