Sendhil Mullainathan, Professor of Computation and Behavioral Science, Chicago Booth


Fragile Algorithms, Fallible People

I will discuss two studies on racial bias as a way to compare and contrast algorithms and people.  Though the studies are on racial bias, the lessons extend more broadly, to many decision-making situations where we fear mistakes or misalignment. My goal is to push us toward an integrative framework that simultaneously thinks about the weaknesses (and strengths) of both algorithms and people. Many current approaches, I fear, fixate on only one side of this coupling.


Sendhil Mullainathan is the Roman Family University Professor of Computation and Behavioral Science and Director of the Center for Applied AI at Chicago Booth. His latest research applies machine learning to social problems, medicine and as a tool for scientific discovery.  In the past, he has worked on topics such as discrimination, the psychology of poverty, and corporate finance.

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), and most recently Nightingale Open Science, a data platform that allows researchers to access medical imaging data.  He has also started several companies (Dandelion Health and Pique). He has worked in government in various roles, and currently serves on the board of the MacArthur Foundation board.