HMI DAIS 16 - Machine Learning for Health and Equity & Health and Equity for Machine Learning
HMI DAIS 16 - Public online seminar, 9am 24 June 2021 AEST
Rumi Chunara is an Associate Professor at NYU, 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, SM from MIT and BSc from Caltech. She is an MIT TR35, NSF Career, Facebook Research and Max Planck Sabbatical award winner and her work has been funded by diverse sources including the Gates Foundation, Rockefeller Foundation, NSF, NIH, and the International Growth Centre.
Seminar Title: Machine Learning for Health and Equity & Health and Equity for Machine Learning
Abstract: As machine learning methods become embedded in society, it has become clear that the data used, objectives selected, and questions we ask are all critical. The study of inequity relays principles that influence this work. First, we assess the incorporation of social determinants, which account for a large and growing proportion of morbidity and mortality worldwide, into machine learning methods to improve health predictions for diverse populations. Second, we elevate ideas of algorithmic fairness by leveraging causal models and incorporating structural factors to better account for and address sources of bias and disparities. A focus on public health, which is concerned with the individual, collective, and environmental factors that affect the health of human populations provides a principled approach spanning data, algorithms and questions to both mitigate bias and proactively design inclusive innovations.
HMI DAIS recordings can be viewed here.