An NBER conference on Economics of Artificial Intelligence took place in Toronto September 13–14. Research Associates Ajay K. Agrawal, Joshua S. Gans, and Avi Goldfarb of University of Toronto and Catherine Tucker of MIT organized the meeting, which was sponsored by the Alfred P. Sloan Foundation, the Canadian Institute for Advanced Research, and the Creative Destruction Lab. These researchers' papers were presented and discussed:
Emilio Calvano, Vencenzo Denicolò, and Sergio Pastorello, University of Bologna, and Giacomo Calzolari, European University Institute, "Q-Learning to Cooperate"
Prasanna Tambe, University of Pennsylvania, "Machine Learning and Domain Knowledge"
Erik Brynjolfsson, MIT and NBER; Tom Mitchell, Carnegie Mellon University; and Daniel Rock, MIT, "Machine Learning and Occupational Change"
Anton Korinek, University of Virginia and NBER, "Artificially Intelligent Agents in Our Economy"
Paul M. Romer, New York University and NBER, "Machine Learning as a 'Wind Tunnel' for Research on Human Learning"
Edmund S. Phelps, Columbia University, "Two Kinds of Robots in Growth Models: An Introduction"
Susan Athey, Stanford University and NBER, "Contextual Bandits"
Matthew Gentzkow, Stanford University and NBER, "Artificial Intelligence, Media, and Fake News"
Sendhil Mullainathan, University of Chicago and NBER, "Using Machine Learning to Understand Human Decision-Making: Application to Health Care"
Kathryn L. Shaw, Stanford University and NBER, "AI and Personnel Economics"
Michael Schwarz, Microsoft, "Open Questions and Research Directions — AI and the Marginal Value of Data"
James Bessen, Boston University, and Robert Seamans, New York University, "Startups' Use of Data for Artificial Intelligence"
Joao Guerreirov, Northwestern University; Sergio Rebelov, Northwestern University and NBER; and Pedro Teles, Banco de Portugal, "Should Robots be Taxed?" (NBER Working Paper No. 23806)
Jason Furman, Harvard Kennedy School, "AI Policy Considerations"
Mitsuru Igami, Yale University, "Artificial Intelligence as Structural Estimation: Economic Interpretations of Deep Blue, Bonanza, and AlphaGo"
Hal Varian, University of California, Berkeley, "Automation v. Procreation"
Isil Erel, Ohio State University; Léa H. Stern, University of Washington; Chenhao Tan, University of Colorado, Boulder; and Michael S. Weisbach, Ohio State University and NBER, "Selecting Directors Using Machine Learning" (NBER Working Paper No. 24435)
Kristina McElheran, University of Toronto, "Economic Measurement of AI"
Bo Cowgill, Columbia University, "Impact of Algorithms on Judicial Discretion: Evidence from Regression Discontinuities"
Daron Acemoglu, MIT and NBER, and Pascual Restrepo, Boston University, "Automation and New Tasks: The Implications of Task Content of Technology for Labor Demand"
Gillian Hadfield, University of Toronto, "Incomplete Contracts and AI Alignment"
Summaries of these papers are at