Sendhil Mullainathan (Chicago Booth): Machine Learning as a Tool for Science

The webinar is on September 28, 9-10am Pacific Time (12-1pm ET)

Presenter: Sendhil Mullainathan (University of Chicago)

Discussant: Sharad Goel (Harvard University)

Zoom webinar link  Webinar ID: 988 9937 7974 Passcode: 636080

Machine Learning as a Tool for Science

Abstract: I will discuss how machine learning can be used to change how we do science. In particular, I will discuss two related two such uses. The first is the automatic generation of hypotheses. While hypothesis testing is a highly formalized activity, hypothesis generation remains largely informal. We propose a systematic procedure to generate novel hypotheses about human behavior, which uses the capacity of machine learning algorithms to notice patterns people might not. We illustrate the procedure with a concrete application: judge decisions about who to jail. The second is the generation of what we call "anomalies". Canonical examples of anomalies include the Allais Paradox and the Kahneman-Tversky choice experiments, which are concrete examples of menus of lotteries that highlighted flaws in expected utility theory and spurred the development of new theories for decision-making under uncertainty. We develop an econometric framework for anomaly generation and develop two algorithmic procedures to generate anomalies (if they exist) when provided a formal theory and data that the theory seeks to explain. Both of these tools are linked by a belief that machine learning can fundamentally transform how we approach the research enterprise.

Bio of speaker: Sendhil Mullainathan is the Roman Family University Professor of Computation and Behavioral Science at Chicago Booth. His current research uses machine learning to understand complex problems in human behavior, social policy, and especially medicine, where computational techniques have the potential to uncover biomedical insights from large-scale health data. He currently teaches a course on Artificial Intelligence. In past work he has combined insights from economics and behavioral science with causal inference tools—lab, field, and natural experiments—to study social problems such as discrimination and poverty. Papers include: the impact of poverty on mental bandwidth; how algorithms can improve on judicial decision-making; whether CEO pay is excessive; using fictitious resumes to measure discrimination; showing that higher cigarette taxes makes smokers happier; and modeling how competition affects media bias.

Mullainathan enjoys writing. He recently co-authored Scarcity: Why Having too Little Means so Much and writes regularly for the New York Times. Additionally, his research has appeared in a variety of publications including the Quarterly Journal of Economics, Science, American Economic Review, Psychological Science, the British Medical Journal, and Management Science. Mullainathan 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, has worked in government in various roles, is affiliated with the NBER and BREAD, and is a member of the American Academy of Arts and Sciences. Prior to joining Booth, Mullainathan was the Robert C. Waggoner Professor of Economics in the Faculty of Arts and Sciences at Harvard University, where he taught courses about machine learning and big data. He began his academic career at the Massachusetts Institute of Technology.

Mullainathan is a recipient of the MacArthur “Genius Grant,” has been designated a “Young Global Leader” by the World Economic Forum, was labeled a “Top 100 Thinker” by Foreign Policy Magazine, and was named to the “Smart List: 50 people who will change the world” by Wired Magazine (UK).

Bio of discussant: Sharad Goel studies public policy through the lens of computer science, bringing a computational perspective to a diverse range of contemporary social issues. His recent work has examined policing practices, including the development of new statistical tests for discrimination; fair machine learning, including guidance for designing equitable algorithms; access to education; and democratic governance, including swing voting, polling errors, voter fraud, and political polarization.

Before joining Harvard, he was on the faculty at Stanford University, with appointments in management science & engineering, computer science, sociology, and the law school. At Stanford, I was the founding director of the Computational Policy Lab. The lab is comprised of researchers, data scientists, and journalists who work to address policy problems through technical innovation. For example, we deployed a “blind charging” platform across California to mitigate racial bias in prosecutorial decisions. We also collected, released, and analyzed data on over 100 million traffic stops as part of our Open Policing Project.

He often writes essays and engages in public discussions on policy issues from a statistical perspective. These include examinations of algorithms in the courts (in the New York Times and the Boston Globe); algorithmic fairness (in the Washington Post and on the Moral Science Podcast); policing (in the Washington Post, Slate, and the Huffington Post); mass incarceration (in the Washington Post); election polls (in the New York Times); claims of voter fraud (in Slate and on This American Life); and college admissions (in the Boston Globe, Boston Review, and the Washington Post).

Sharad holds an undergraduate degree in mathematics from the University of Chicago, as well as a doctorate in applied math and a master’s in computer science from Cornell University. After finishing graduate school, he completed postdoctoral fellowships in the math departments at Stanford and the University of Southern California, and then worked as a research scientist at Yahoo and Microsoft before returning to academia.