NBER Conference Toronto 2022
NBER Economics of Artificial Intelligence
Toronto, 22nd-23rd September 2022
Thursday, September 22
James J. Feigenbaum, Boston University and NBER
Daniel P. Gross, Duke University and NBER
View abstract
This paper was distributed as Working Paper 29580, where an updated version may be available.
Discussant:
Joshua S. Gans, University of Toronto and NBER
Technological Complements to AI Growth
Daniel Rock, University of Pennsylvania
Prasanna Tambe, University of Pennsylvania
Zhiwei Wang, University of Pennsylvania
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Discussant:
Florenta Teodoridis, University of Southern California
(Don’t) Take Me Home: Home Bias and the Effect of Self-Driving Trucks on Interstate Trade
Ron Yang, Stanford University
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Discussant:
Thomas N. Hubbard, Northwestern University and NBER
Augmented Intelligence: The Effects of AI on Productivity and Work Practices
Lindsey R. Raymond, Massachusetts Institute of Technology
Erik Brynjolfsson, Stanford University and NBER
Danielle Li, Massachusetts Institute of Technology and NBER
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Discussant:
Emma J. Pierson, Cornell University
Artificial Intelligence and Auction Design
Martino Banchio, Stanford University
Andrzej Skrzypacz, Stanford University
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Discussant:
Emilio Calvano, University of Bologna
Competitive Algorithmic Targeting and Model Selection
Ganesh Iyer, University of California, Berkeley
Tony Ke, Chinese University of Hong Kong
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Discussant:
Jeanine Miklós-Thal, University of Rochester
Does Access to an Algorithmic Decision-making Tool Change Child Protective Service Caseworkers’ Investigation Decisions?
Maria D. Fitzpatrick, Cornell University and NBER
Katharine Sadowski, Cornell University
Christopher Wildeman, Cornell University
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Discussant:
Ashley T. Swanson, University of Wisconsin–Madison and NBER
Artificial Intelligence, Firm Growth, and Product Innovation
Tania Babina, Columbia University and NBER
Alex X. He, University of Maryland
Anastassia Fedyk, University of California at Berkeley
James Hodson, AI for Good
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Discussant:
Erik Brynjolfsson, Stanford University and NBER
Friday, September 23
When Algorithms Explain Themselves: AI Adoption and Accuracy of Experts' Decisions
Himabindu Lakkaraju, Stanford University
Chiara Farronato, Harvard University and NBER
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Discussant:
Kate Bundorf, Duke University and NBER
Identifying Prediction Mistakes in Observational Data
Ashesh Rambachan, Massachusetts Institute of Technology
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Discussant:
Maria Polyakova, Stanford University and NBER
Health Data Platforms
Ziad Obermeyer, University of California, Berkeley and NBER
Sendhil Mullainathan, University of Chicago and NBER
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Discussants:
Judy Gichoya, Emory University
David Donoho, Stanford University
David Dranove, Northwestern University
Craig Garthwaite, Northwestern University and NBER
View abstract
This paper was distributed as Working Paper 30607, where an updated version may be available.
Discussants:
David Meltzer, University of Chicago and NBER
Parvin Mousavi, Queen's University
AI to Reduce Administrative Costs in Healthcare
David M. Cutler, Harvard University and NBER
Nikhil Sahni, McKinsey & Company
George Stein, McKinsey & Company
Rodney Zemmel, McKinsey & Company
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Discussants:
Mark Sendak, Duke Institute for Health Innovation
David C. Chan Jr, Stanford University and NBER
The Regulation of Medical AI: Policy Approaches, Data, and Innovation Incentives
Ariel Dora Stern, Harvard University
View abstract
This paper was distributed as Working Paper 30639, where an updated version may be available.
Discussants:
Boris Babic, University of Toronto
Anna Goldenberg, University of Toronto