Stanford University


Sendhil Mullainathan
Algorithmic Behavioral Science: Automated Discovery of Human Biases

Sendhil Mullainathan: Algorithmic Behavioral Science: Automated Discovery of Human Biases
November 15, 2021
Hybrid Event
Koret-Taube Room 120 at SIEPR
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On November 15, 2021, Professor Sendhil Mullainathan of The University of Chicago Booth School of Business joined S-DEL Director Erik Brynjolfsson to discuss the automated discovery of human biases.


Algorithmic Behavioral Science:
Automated Discovery of Human Biases

Science begins with something curiously non-scientific. Scientists meticulously test hypotheses that themselves come from a very messy place: a mix of creativity, intuition, observation and chance. We argue machine learning can play a more rigorous role here. We illustrate this in a problem that is of great conceptual and practical interest: how do judges decide whom to jail? A deep learning algorithm trained on past data discovers a striking behavioral error: a defendant’s face alone accounts for 30% of the explainable variation in whom judges choose to jail. This finding is not explained by race, skin color, demographics or other known factors. To make the discovery usable, we develop a procedure that allows the algorithm to communicate what it is seeing in the face. This leads us to identify facial features, previously not considered, that bias the way judges treat defendants.

About Sendhil Mullainathan

Sendhil Mullainathan

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 Meansso Muchand 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).

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