Clinical Algorithmic Discrimination
Learning Goals:
- Define algorithmic discrimination
- Overview concerns regarding algorithmic discrimination
- Understand factors leading to algorithmic discrimination
- When should we include/exclude race as a predictor in algorithms
- Understanding pathways to minimize algorithmic discrimination
Anirban Basu is a Professor of Health Economics and the Stergachis Family Endowed Director of The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute at the University of Washington, Seattle, with joint appointments in the Departments of Health Systems & Population Health and Economics. He is a Research Associate at the US National Bureau of Economic Research and an elected Fellow at the American Statistical Association. His work sits at the intersection of microeconomics, statistics, and health policy. His research focuses on understanding the economic value of health care, generating causal evidence, and, lately, on the potential for discrimination with machine learning and artificial intelligence algorithms. He served on the 2nd Panel on Cost-effectiveness Analysis in Health and Medicine and serves on the Editorial Advisory Board for Value in Health Journal. He received his MS in Biostatistics from UNC-Chapel Hill and a Ph.D. in Public Policy Studies from the University of Chicago.
