I am Gordon McKay Professor of Computer Science at Harvard University. I am a member of the EconCS Group; I am also affiliated with the Center for Research on Computation and Society, the Ash Center for Democratic Governance and Innovation, the Institute for Quantitative Social Science and the Center of Mathematical Sciences and Applications. Before joining Harvard in 2020, I had been a faculty member in the Computer Science Department at Carnegie Mellon University.
I work on a broad and dynamic set of problems related to AI, algorithms, economics, and society. I am especially excited about projects that involve both interesting theory and direct applications; examples include the websites Spliddit (temporarily inactive) and Panelot, as well as recent collaborations with nonprofit organizations such as Refugees.AI, 412 Food Rescue, and the Sortition Foundation.
Not-for-profit service that provides solutions to everyday fair division problemstemporarily inactive
Schwartz Reisman Institute Seminar, March 2022
Sortition is a storied paradigm of democracy built on the idea of choosing representatives through lotteries instead of elections. In recent years this idea has found renewed popularity in the form of citizens’ assemblies, which bring together randomly selected people from all walks of life to discuss key questions and deliver policy recommendations. A principled approach to sortition, however, must resolve the tension between two competing requirements: that the demographic composition of citizens’ assemblies reflect the general population and that every person be given a fair chance (literally) to participate. I will describe our work on designing, analyzing and implementing randomized participant selection algorithms that balance these two requirements. I will also discuss practical challenges in sortition based on experience with the adoption and deployment of our open-source system, Panelot.
MD4SG Workshop, June 2019
I will present the 'virtual democracy' framework for the design of ethical AI. In a nutshell, the framework consists of three steps: first, collect preferences from voters on example dilemmas; second, learn models of their preferences, which generalize to any (previously unseen) dilemma; and third, at runtime, predict the voters' preferences on the current dilemma, and aggregate these virtual 'votes' using a voting rule to reach a decision. I will focus on two instantiations of this approach: a proof-of concept system that decides ethical dilemmas potentially faced by autonomous vehicles, and a decision support tool designed to help a Pittsburgh-based nonprofit allocate food donations to recipient organizations. These projects bridge AI, social choice theory, statistics, and human-computer interaction; I will discuss challenges in all of these areas.
University of Washington CS Colloquium, November 2017
Computational social choice deals with algorithms for aggregating individual preferences or opinions towards collective decisions. AI researchers (including myself) have long argued that such algorithms could play a crucial role in the design and implementation of multiagent systems. However, in the last few years I have come to realize that the "killer app" of computational social choice is helping people — not software agents — make joint decisions. I will illustrate this theme through two recent endeavors: Spliddit.org, a website that offers provably fair solutions to everyday problems; and Robovote.org, which provides optimization-driven voting methods. Throughout the talk, I will devote special attention to the theoretical foundations and results that make these services possible.