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AIMI Invited Lecture: Dissecting Algorithmic Bias - Ziad Obermeyer, MD

Event Details:

Tuesday, March 30, 2021
3:00pm - 4:00pm PDT


REGISTER FOR THIS FREE EVENT HERE.

Abstract:

There is more and more attention to how algorithms can reproduce and even scale up racial bias. Using examples from my own work, I'll show that a major mechanism by which bias gets into algorithms is via the choice of biased prediction targets (labels). I'll also argue that by retraining algorithms on less biased proxies, they can be turned into forces to reduce disparities rather than perpetuate them.

About:
Ziad Obermeyer is the Blue Cross of California Distinguished Associate Professor of Health Policy and Management in the School of Public Health at UC Berkeley, where he does research at the intersection of machine learning, medicine, and health policy. He was named an Emerging Leader in Health and Medicine by the National Academy of Medicine, and received numerous awards including the Early Independence Award -- the National Institutes of Health’s most prestigious award for exceptional junior scientists -- and the Young Investigator Award from the Society for Academic Emergency Medicine. Previously, he was an Assistant Professor at Harvard Medical School. He continues to practice emergency medicine in underserved communities. His work has been published in Science, The New England Journal of Medicine, JAMA, The BMJ, and Health Affairs, and his research has been supported by the National Institutes of Health, Schmidt Futures, the Gordon and Betty Moore Foundation, the Robert Wood Johnson Foundation, and the Laura and John Arnold Foundation. Prior to his career in medicine, he worked as a consultant to pharmaceutical and global health clients at McKinsey & Co. in New Jersey, Geneva, and Tokyo. He is a graduate of Harvard College (magna cum laude) and Harvard Medical School (magna cum laude), and earned an M.Phil. from Cambridge.

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