The economics of artificial intelligence : an agenda / Ajay Agrawal, Joshua Gans, Avi Goldfarb.

Format:

Book

Conference/Event

Contributor:

Agrawal, Ajay, Editor.

Gans, Joshua, 1968- Editor.

Goldfarb, Avi, Editor.

Conference Name:

National Bureau of Economic Research Conference on the Economics of Artificial Intelligence (2017 : Toronto, Canada), associated with work.

Series:

National Bureau of Economic Research conference report.

National Bureau of Economic Research Conference Report.

Chicago scholarship online.

Language:

English

Subjects (All):

Artificial intelligence--Economic aspects.

Artificial intelligence.

Physical Description:

1 online resource (xi, 630 pages)

Place of Publication:

Chicago : University of Chicago Press, [2019]

Language Note:

In English.

Summary:

Advances in artificial intelligence (AI) highlight the potential of this technology to affect productivity, growth, inequality, market power, innovation, and employment. This volume seeks to set the agenda for economic research on the impact of AI. It covers four broad themes: AI as a general purpose technology; the relationships between AI, growth, jobs, and inequality; regulatory responses to changes brought on by AI; and the effects of AI on the way economic research is conducted. It explores the economic influence of machine learning, the branch of computational statistics that has driven much of the recent excitement around AI, as well as the economic impact of robotics and automation and the potential economic consequences of a still-hypothetical artificial general intelligence. The volume provides frameworks for understanding the economic impact of AI and identifies a number of open research questions. Contributors: Daron Acemoglu, Massachusetts Institute of Technology Philippe Aghion, Collège de France Ajay Agrawal, University of Toronto Susan Athey, Stanford University James Bessen, Boston University School of Law Erik Brynjolfsson, MIT Sloan School of Management Colin F. Camerer, California Institute of Technology Judith Chevalier, Yale School of Management Iain M. Cockburn, Boston University Tyler Cowen, George Mason University Jason Furman, Harvard Kennedy School Patrick Francois, University of British Columbia Alberto Galasso, University of Toronto Joshua Gans, University of Toronto Avi Goldfarb, University of Toronto Austan Goolsbee, University of Chicago Booth School of Business Rebecca Henderson, Harvard Business School Ginger Zhe Jin, University of Maryland Benjamin F. Jones, Northwestern University Charles I. Jones, Stanford University Daniel Kahneman, Princeton University Anton Korinek, Johns Hopkins University Mara Lederman, University of Toronto Hong Luo, Harvard Business School John McHale, National University of Ireland Paul R. Milgrom, Stanford University Matthew Mitchell, University of Toronto Alexander Oettl, Georgia Institute of Technology Andrea Prat, Columbia Business School Manav Raj, New York University Pascual Restrepo, Boston University Daniel Rock, MIT Sloan School of Management Jeffrey D. Sachs, Columbia University Robert Seamans, New York University Scott Stern, MIT Sloan School of Management Betsey Stevenson, University of Michigan Joseph E. Stiglitz. Columbia University Chad Syverson, University of Chicago Booth School of Business Matt Taddy, University of Chicago Booth School of Business Steven Tadelis, University of California, Berkeley Manuel Trajtenberg, Tel Aviv University Daniel Trefler, University of Toronto Catherine Tucker, MIT Sloan School of Management Hal Varian, University of California, Berkeley

Contents:

Frontmatter

Contents

Acknowledgments

Introduction

1. Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics

2. The Technological Elements of Artificial Intelligence

3. Prediction, Judgment, and Complexity: A Theory of Decision-Making and Artificial Intelligence

4. The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis

5. Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth

6. Artificial Intelligence as the Next GPT: A Political-Economy Perspective

7. Artificial Intelligence, Income, Employment, and Meaning

8. Artificial Intelligence, Automation, and Work

9. Artificial Intelligence and Economic Growth

10. Artificial Intelligence and Jobs: The Role of Demand

11. Public Policy in an AI Economy

12. Should We Be Reassured If Automation in the Future Looks Like Automation in the Past?

13. R&D, Structural Transformation, and the Distribution of Income

14. Artificial Intelligence and Its Implications for Income Distribution and Unemployment

15. Neglected Open Questions in the Economics of Artificial Intelligence

16. Artificial Intelligence, Economics, and Industrial Organization

17. Privacy, Algorithms, and Artificial Intelligence

18. Artificial Intelligence and Consumer Privacy

19. Artificial Intelligence and International Trade

20. Punishing Robots: Issues in the Economics of Tort Liability and Innovation in Artificial Intelligence

21. The Impact of Machine Learning on Economics

22. Artificial Intelligence, Labor, Productivity, and the Need for Firm-Level Data

23. How Artificial Intelligence and Machine Learning Can Impact Market Design

24. Artificial Intelligence and Behavioral Economics

Contributors

Author Index

Subject Index

Notes:

Previously issued in print: 2019.

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (publisher's Web site, viewed 06. Apr 2020)

ISBN:

9780226613475

022661347X

OCLC:

1099435014