Division of Biostatistics Seminar – Algorithmic Bias and Machine Learning in Health Care
Health care has moved toward analytic systems that take large databases and estimate varying quantities of interest both quickly and robustly, incorporating advances from statistics, econometrics, and computer science. The massive size of the health care sector make data science applications particularly salient for social policy. This presentation will discuss the intersection of health equity and statistical machine learning algorithms for health economics and health services research with emphasis on groups marginalized by the health care system. Considerations go well beyond typical measures of statistical assessment, and focus on concepts such as algorithmic fairness as well as the need for enforced minimum standards for research quality. Overarching themes are that centering health equity and developing methodology tailored to specific health questions are critical given the stakes involved.
All are Welcome.
Sherri Rose, PhD, is a Professor of Health Policy whose research is centered on developing and integrating innovative statistical machine learning approaches to improve human health and health equity.