Events
Seminar @ Cornell Tech: Sendhil Mullainathan
Doing Good and Avoiding Harm: Lessons from Applying ML to Social Problems
For those concerned with having positive social impact, there are many problems where machine learning algorithms can potentially do enormous good. But because they sit outside the usual application domains and companies, most of these problems are largely ignored. Such problems are additionally interesting because the applications are by no means mechanical. Since they involve interacting with existing decision systems, they raise interesting technical and conceptual challenges. Here I describe our team’s experience working on a few such problems, where the individual applications have value in their own right but also serve to illustrate the kinds of conceptual issues we have been facing. Amongst the issues that arise are a deeper understanding of potential for algorithmic bias (and how it interacts with human bias), overlooked statistical confounders and measurement error. I will also suggest data-rich areas ripe for application.
Speaker Bio
Sendhil Mullainathan is the Roman Family University Professor of Computation and Behavioral Science at Chicago Booth. His latest research is on computational medicine—applying machine learning and other data science tools to produce biomedical insights. In past work he has combined insights from behavioral science with empirical methods—experiments, causal inference tools, and machine learning—to study social problems such as discrimination and poverty. He currently teaches a course on Artificial Intelligence.
Outside of research, he co-founded a non-profit to apply behavioral science (ideas42), a center to promote the use of randomized control trials in development (the Abdul Latif Jameel Poverty Action Lab), has worked in government in various roles, and currently serves on the board of the MacArthur Foundation board. He is also a regular contributor to the New York Times.