Algorithmic fairness and the science of health disparities

Rumi Chunara / New York University

Abstract: It has been shown that equalizing health disparities can avert more deaths than the number of lives saved by medical advances alone in the same time frame. Moreover, without a simultaneous focus on innovations and equity, advances in health for one group can occur at the cost of added challenges for another. In this talk I will introduce the science of health disparities and juxtapose it with the machine learning subfield of algorithmic fairness. Given the key foci and principles of health equity and health disparities within public and population health, I will show examples of how machine learning and principles of public and population health can be synergized for using data to advance the science of health disparities and sustainable health of entire populations.

Bio: Dr. Rumi Chunara is an Associate Professor at New York University, jointly appointed at the Tandon School of Engineering (in Computer Science) and the School of Global Public Health (in Biostatistics/Epidemiology). Her PhD is from the Harvard-MIT Division of Health Sciences and Technology and her BSc from Caltech. Her research group focuses on developing computational and statistical approaches for acquiring, integrating and using data to improve population and public health. She is an MIT TR35, NSF Career, Bill & Melinda Gates Foundation Grand Challenges, Facebook Research and Max Planck Sabbatical award winner.