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Economic Policy for Artificial Intelligence

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  • Ajay K. Agrawal
  • Joshua S. Gans
  • Avi Goldfarb

Abstract

Recent progress in artificial intelligence (AI) – a general purpose technology affecting many industries - has been focused on advances in machine learning, which we recast as a quality-adjusted drop in the price of prediction. How will this sharp drop in price impact society? Policy will influence the impact on two key dimensions: diffusion and consequences. First, in addition to subsidies and IP policy that will influence the diffusion of AI in ways similar to their effect on other technologies, three policy categories - privacy, trade, and liability - may be uniquely salient in their influence on the diffusion patterns of AI. Second, labor and antitrust policies will influence the consequences of AI in terms of employment, inequality, and competition.

Suggested Citation

  • Ajay K. Agrawal & Joshua S. Gans & Avi Goldfarb, 2018. "Economic Policy for Artificial Intelligence," NBER Working Papers 24690, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:24690
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    1. Mitchell Hoffman & Lisa B Kahn & Danielle Li, 2018. "Discretion in Hiring," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 765-800.
    2. Bresnahan, Timothy F. & Trajtenberg, M., 1995. "General purpose technologies 'Engines of growth'?," Journal of Econometrics, Elsevier, vol. 65(1), pages 83-108, January.
    3. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
    4. Nicholas Bloom & Charles I. Jones & John Van Reenen & Michael Webb, 2020. "Are Ideas Getting Harder to Find?," American Economic Review, American Economic Association, vol. 110(4), pages 1104-1144, April.
    5. Heidi L. Williams, 2016. "Intellectual Property Rights and Innovation: Evidence from Health Care Markets," Innovation Policy and the Economy, University of Chicago Press, vol. 16(1), pages 53-87.
    6. Jerry R. Green & Suzanne Scotchmer, 1995. "On the Division of Profit in Sequential Innovation," RAND Journal of Economics, The RAND Corporation, vol. 26(1), pages 20-33, Spring.
    7. Avi Goldfarb & Catherine Tucker, 2012. "Privacy and Innovation," NBER Chapters, in: Innovation Policy and the Economy, Volume 12, pages 65-89, National Bureau of Economic Research, Inc.
    8. Josh Lerner & Scott Stern, 2016. "Innovation Policy and the Economy, Volume 16," NBER Books, National Bureau of Economic Research, Inc, number lern15-1.
    9. Nicholas Crafts, 2004. "Steam as a general purpose technology: A growth accounting perspective," Economic Journal, Royal Economic Society, vol. 114(495), pages 338-351, April.
    10. Betsey Stevenson, 2018. "Artificial Intelligence, Income, Employment, and Meaning," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 189-195, National Bureau of Economic Research, Inc.
    11. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation, and Work," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 197-236, National Bureau of Economic Research, Inc.
    12. Hal Varian, 2018. "Artificial Intelligence, Economics, and Industrial Organization," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 399-419, National Bureau of Economic Research, Inc.
    13. David Autor & David Dorn & Lawrence F Katz & Christina Patterson & John Van Reenen, 2020. "The Fall of the Labor Share and the Rise of Superstar Firms [“Automation and New Tasks: How Technology Displaces and Reinstates Labor”]," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 135(2), pages 645-709.
    14. Matt Taddy, 2018. "The Technological Elements of Artificial Intelligence," NBER Working Papers 24301, National Bureau of Economic Research, Inc.
    15. Philippe Aghion & Benjamin F. Jones & Charles I. Jones, 2018. "Artificial Intelligence and Economic Growth," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 237-282, National Bureau of Economic Research, Inc.
    16. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation and Work," Boston University - Department of Economics - Working Papers Series dp-298, Boston University - Department of Economics.
    17. Matt Taddy, 2018. "The Technological Elements of Artificial Intelligence," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 61-87, National Bureau of Economic Research, Inc.
    18. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    19. Jason Furman & Robert Seamans, 2019. "AI and the Economy," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 161-191.
    20. Manuel Trajtenberg, 2018. "Artificial Intelligence as the Next GPT: A Political-Economy Perspective," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 175-186, National Bureau of Economic Research, Inc.
    21. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2018. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 23-57, National Bureau of Economic Research, Inc.
    22. Trajtenberg, Manuel, 2018. "AI as the next GPT: a Political-Economy Perspective," CEPR Discussion Papers 12721, C.E.P.R. Discussion Papers.
    23. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 115-146, National Bureau of Economic Research, Inc.
    24. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2018. "Introduction to "The Economics of Artificial Intelligence: An Agenda"," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 1-19, National Bureau of Economic Research, Inc.
    25. Timothy F. Bresnahan & Erik Brynjolfsson & Lorin M. Hitt, 2002. "Information Technology, Workplace Organization, and the Demand for Skilled Labor: Firm-Level Evidence," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(1), pages 339-376.
    26. Petra Moser, 2005. "How Do Patent Laws Influence Innovation? Evidence from Nineteenth-Century World's Fairs," American Economic Review, American Economic Association, vol. 95(4), pages 1214-1236, September.
    27. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
    28. Alberto Galasso & Hong Luo, 2018. "Punishing Robots: Issues in the Economics of Tort Liability and Innovation in Artificial Intelligence," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 493-504, National Bureau of Economic Research, Inc.
    29. Mitsuru Igami, 2017. "Artificial Intelligence as Structural Estimation: Economic Interpretations of Deep Blue, Bonanza, and AlphaGo," Papers 1710.10967, arXiv.org, revised Mar 2018.
    30. Mitsuru Igami, 0. "Artificial intelligence as structural estimation: Deep Blue, Bonanza, and AlphaGo," Econometrics Journal, Royal Economic Society, vol. 23(3), pages 1-24.
    31. Daron Acemoglu & Pascual Restrepo, 2017. "Robots and Jobs: Evidence from US Labor Markets," Boston University - Department of Economics - Working Papers Series dp-297, Boston University - Department of Economics.
    32. Fiona Scott Morton & Carl Shapiro, 2016. "Patent Assertions: Are We Any Closer to Aligning Reward to Contribution?," Innovation Policy and the Economy, University of Chicago Press, vol. 16(1), pages 89-133.
    33. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
    34. David, Paul A, 1990. "The Dynamo and the Computer: An Historical Perspective on the Modern Productivity Paradox," American Economic Review, American Economic Association, vol. 80(2), pages 355-361, May.
    35. Jason Furman, 2018. "Should We Be Reassured If Automation in the Future Looks Like Automation in the Past?," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 317-328, National Bureau of Economic Research, Inc.
    36. Joshua S. Gans, 2018. "Self-Regulating Artificial General Intelligence," NBER Working Papers 24352, National Bureau of Economic Research, Inc.
    37. David H. Autor & Lawrence F. Katz & Alan B. Krueger, 1998. "Computing Inequality: Have Computers Changed the Labor Market?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(4), pages 1169-1213.
    38. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation," NBER Working Papers 24449, National Bureau of Economic Research, Inc.
    39. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
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    JEL classification:

    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

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