Equitable Algorithms: A Social Welfare Function Approach
We take a social welfare analysis approach to the problem of designing algorithms that are equitable. In our framework, the social planner cares both about the efficiency and equity of outcomes. These preferences induce a preference over algorithms; as a result, in our model, the notion of the fairness of an algorithm is derived from these more primitive utilitarian preferences rather than defined ex ante. Our characterization of these implied preferences allow us to then address several questions. First, we describe how algorithms ought to be “procured”; e.g., how a city that cares about acquiring fair algorithms for bail decisions would run a Netflix-style competition. Second, we derive optimal regulatory policy for governments that seek to regulate algorithm choices of private actors (e.g. companies) that care only narrowly about efficiency. Third, we illustrate these ideas using empirical data from criminal justice, education, and health care. Finally, we use the framework to talk about the equity consequences of simplicity in the design of algorithms.
Sendhil Mullainathan is university professor and professor of computation and behavioral science at Chicago Booth School of Business. 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.
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, American Economic Review, Psychological Science, Science, British Medical Journal, Management Science, and the KDD Proceedings.
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, 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 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).