Sendhil Mullainathan, Professor of Economics, Harvard University.
There is a class of policy problems that require predictive tools, rather than causal tools to solve. As such as they are ideal for machine learning applications.
I will illustrate with the case of bail decisions and highlight that (1) the potential social gain for this approach is high; (2) there is an intimate link to behavioral economics; and (3) new issues for machine learning arise in solving these problems.
Finally I will try to argue that prediction policy problems are quite common and as conceptually interesting as causal problems.
About the speaker
Sendhil Mullainathan is a Professor of Economics at Harvard University. His real passion is behavioral economics. His work runs a wide gamut: the impact of poverty on mental bandwidth; whether CEO pay is excessive; using fictitious resumes to measure discrimination; showing that higher cigarette taxes makes smokers happier; modeling how competition affects media bias; and a model of coarse thinking. His latest research focuses on using machine learning and data mining techniques to better understand human behavior.
He enjoys writing, having recently co-authored Scarcity: Why Having too Little Means so Much and writes regularly for the New York Times. The current issue of Harvard Magazine (May 2015) features an in-depth profile and discussion of this work.
He helped co-found a non-profit to apply behavioral science (ideas42), co-founded a center to promote the use of randomized control trials in development (the Abdul Latif Jameel Poverty Action Lab), serves on the board of the MacArthur Foundation, and has worked in government in various roles, including most recently as Assistant Director of Research at the Consumer Financial Protection Bureau.
He is a recipient of the MacArthur “genius” Award, has been designated a “Young Global Leader” by the World Economic Forum, labeled a “Top 100 Thinker” by Foreign Policy Magazine, and named to the “Smart List: 50 people who will change the world” by Wired Magazine (UK). His hobbies include basketball, board games, googling and fixing-up classic espresso machines.