Lecture Title: Machine Learning as a Tool for Scientific Discovery: Applications to Behavioral Economics and Medicine
Presented By: Sendhil Mullainathan, University Professor, Professor of Computation and Behavioral Science, and George C. Tiao Faculty Fellow
The University of Chicago Booth School of Business
Location: Bahen Centre, 40 St. George Street, Room 1180
Abstract: I will describe efforts I have been involved in to use machine learning as a way to push the frontier of scientific understanding. The goal in these projects is to both test existing theories or to generate new ones. In testing hypotheses, these algorithms prove particularly useful because they allow us to go past testing the accuracy of a theory to quantifying its completeness. I illustrate using data on human biases in understanding randomness - a topic of long-standing interest in behavioral science. We find that existing models explain only a small fraction of the explainable variance. These algorithms also prove helpful in generating new hypothesis because they can detect signal the human mind and existing theoriesignore. I illustrate with rich EKG data that we model using a deep learning pipeline. The resulting algorithm manages to find unexpected signal in the EKGs for social and physiological outcomes---signal that is not predicted by existing theories. These discoveries raises an interpretability issue - what is the algorithm seeing and what new mechanisms does it suggest? In this context, interpretability takes on a slight twist. The goal is not to make the algorithm palatable to human intuitions - or to build trust. Instead the goal is to find the part of the algorithm that is counter-intuitive, so as to generate new hypotheses. We develop a technique ("anchored representations') that makes black box algorithms interpretable in this way. When applied to our EKG findings and shown to clinicians, intriguing hypotheses arise. As a whole, this work has not just suggested not just the utility of algorithms but raised new conceptual questions.
Biography: Sendhil Mullainathan is a University Professor and Professor of Computation and Behavioral Science at Chicago Booth. He has worked on machine learning, poverty, finance behavioral economics and a wide variety of topics such as: 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 to better understand human behavior.
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The Department of Computer Science Distinguished Lecture Series is named the “C.C. ‘Kelly’ Gotlieb Distinguished Lecture Series” in honour of the late Professor Emeritus (1921-2016) and first department chair (1964-68) who is widely regarded as the "Father of Computing in Canada".This series promotes distinguished scholarship in the field.