Elaine O. Nsoesie

AI and Health Equity

Summary

Data and algorithms can be used to reduce, eliminate, or worsen health inequity. It is therefore important to understand the factors that impact how a particular dataset is collected and the potential impacts of using biased data in developing AI models. This talk will focus on bias in health data and AI algorithms, and the need to create new structures that ensure that data and algorithms are used to redress health inequity.

Short bio

Elaine O. Nsoesie is an Assistant Professor at Boston University School of Public Health and an Assistant Director of Research at Boston University Center for Antiracist Research. She is also on IPA at the National Institutes of Health where she co-leads the AIM-AHEAD program. She is a founding member of the Faculty of Computing & Data Sciences, and a Data Science Faculty Fellow at Boston University. She has a PhD in Computational Epidemiology, MS in Statistics and BS in Mathematics. Her research is focused on the use of data and technology to address racial and health inequity. She is the founder of Rethé (rethe.org) – an initiative focused on providing scientific writing tools and resources to student communities in Africa to increase representation in scientific publications. She has written for NPR, The Conversation, Public Health Post, and Think Global Health. Elaine was born and raised in Cameroon.