Program Description
Event Details
Ethics and Equity in Data Science and Precision Health
A discussion led by Bhramar Mukherjee, PhD, Senior Associate Dean of Public Health Data Science and Data Equity; Anna M.R. Lauder Professor of Biostatistics; Professor of Epidemiology (Chronic Diseases) and of Statistics and Data Science Yale University.
The Data Struggle of the Unseen
Despite several proposed roadmaps to increase representation in scientific research, most of the world's research data are collected on selected populations. We rely on summary statistics from well-represented groups and then devise clever statistical methods to transfer/transport them for cross-ancestry use. In this talk, I would first argue the obvious: for building fair algorithms we need representative training datasets. As public health statisticians, our job is not just to predict, but to prevent. With artificial intelligence tools influencing our daily decisions and scholarship, critical appraisal of data and data science products is critically important for disease prevention and care. I will end the talk with a call to arms for statisticians and computational scientists to not just develop new technical methods but also lead efforts for creating, curating, collecting data and pioneering new scientific studies, stepping outside their comfort zones. Examples from COVID-19, cancer and environmental health will form the thread of “data stories”.