TAP Scholars Discuss the Economics of AI

By TAP Staff Blogger

Posted on April 30, 2019


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Artificial intelligence (AI) technologies have advanced rapidly over the last several years. As the technology continues to improve, it may have a substantial impact on the economy with respect to productivity, growth, inequality, market power, innovation, and employment. ... Without a better understanding of how AI might impact the economy, we cannot design policy to prepare for these changes.
  -  from The Economics of Artificial Intelligence: An Agenda, edited by Ajay Agrawal, Joshua Gans, and Avi Goldfarb (The University of Chicago Press, forthcoming May 2019)

 

In the fall of 2017, the National Bureau of Economic Research held its first conference on the Economics of Artificial Intelligence. The purpose of the conference was to set the research agenda for economists working on AI. Scholars who accepted the invitation were asked to write up and present ideas around a specific topic related to their expertise. The conference was unique because it emphasized the work that still needs to be done, rather than the presentation of standard research papers.

 

Conference organizers Ajay Agrawal, Joshua Gans, and Avi Goldfarb, all University of Toronto Rotman School of Management professors, have compiled and edited the proceedings of the conference into a new book coming out this May. The Economics of Artificial Intelligence: An Agenda seeks to set the agenda for the economic research on the impact of AI. The book 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.”

 

Several TAP economists participated in the conference and provided chapters for the book. Below is a list of the scholars and their articles with a brief summary.

 

Before delving into the individual chapters from TAP scholars, the below excerpt from the introduction to the book explains what is meant by ‘artificial intelligence.’

 

Introduction to "The Economics of Artificial Intelligence: An Agenda"
Written by Ajay Agrawal – Associate Professor, Strategic Management at the University of Toronto Rotman School of Management; Joshua S. Gans – the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship, as well as Professor of Strategic Management, at the University of Toronto Rotman School of Management; and, Avi Goldfarb – Associate Professor of Marketing at the University of Toronto Rotman School of Management

 

The chapters in this volume discuss three related, but distinct, concepts of artificial intelligence. First, there is the technology that has driven the recent excitement around artificial intelligence: Machine learning. Machine learning is a branch of computational statistics. It is a tool of prediction in the statistical sense, taking information you have and using it to fill in information you do not have. Since 2012, the uses of machine learning as a prediction technology have grown substantially. One set of machine learning algorithms in particular, called “deep learning”, has been shown to be useful and commercially viable for a variety of prediction tasks from search engine design to image recognition to language translation. …

 

While the recent interest in AI is driven by machine learning, computer scientists and philosophers have emphasized the feasibility of a true artificial general intelligence that equals or exceeds human intelligence. … The economic and societal impact of machines that surpass human intelligence would be extraordinary. Therefore—whether such an event occurs imminently, in a few decades, in a millennium, or never—it is worth exploring the economic consequences of such an event. While not a focal aspect of any chapter, several of the chapters in this volume touch on the economic consequences of such superintelligent machines.

 

A third type of technology that is often labeled “artificial intelligence” is better-seen as a process: automation. Much of the existing empirical work on the impact of artificial intelligence uses data on factory automation through robotics. … Automation is a potential consequence of artificial intelligence, rather than artificial intelligence per se. Nevertheless, discussions of the consequences of artificial intelligence and automation are tightly connected.

 

While most chapters in the book focus on the first definition—artificial intelligence as machine learning—a prediction technology, the economic implications of artificial general intelligence and automation receive serious attention.

 

Chapters in The Economics of Artificial Intelligence: An Agenda Written by TAP Scholars

 

Artificial Intelligence as a General Purpose Technology (GPT)

 

Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics
Written by Erik Brynjolfsson – Director of the MIT Initiative on the Digital Economy and Professor at MIT Sloan School; Daniel Rock – Post-Doctoral Researcher, MIT Initiative on the Digital Economy; and, Chad Syverson – the Eli B. and Harriet B. Williams Professor of Economics, University of Chicago Booth School of Business

 

Artificial intelligence is advancing rapidly, but productivity growth has been falling for a decade, and real income has stagnated. The most plausible explanation is that it will take considerable time for AI-related technologies to be deployed throughout the economy.

 

Prediction, Judgment, and Complexity: A Theory of Decision Making and Artificial Intelligence
Written by Ajay Agrawal – Associate Professor, Strategic Management at the University of Toronto Rotman School of Management; Joshua S. Gans – the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship, as well as Professor of Strategic Management, at the University of Toronto Rotman School of Management; and, Avi Goldfarb – Associate Professor of Marketing at the University of Toronto Rotman School of Management

 

Artificial intelligence raises the possibility that machines will substitute for humans. AI improves the accuracy of predictions, but when a prediction cannot be made with absolute certainty, judgment is needed to choose the best course of action. Judgment can sometimes be automated.

 

Growth, Jobs, and Inequality

 

Artificial Intelligence, Automation, and Work
Written by Daron Acemoglu – the Elizabeth and James Killian Professor of Economics at Massachusetts Institute of Technology; and, Pascual Restrepo – Assistant Professor at Boston University

 

Automation tends to displace human workers, reducing wages by reducing the demand for labor. But automation also increases productivity and creates new-labor intensive tasks. Several factors constrain the labor market’s capacity to adjust, especially if automation proceeds too quickly.

 

AI and Jobs: The Role of Demand” (chapter title: “Artificial Intelligence and Jobs: The Role of Demand”)
Written by James Bessen, Lecturer and Executive Director, Technology & Policy Research Initiative at the Boston University School of Law

 

In recent decades, technology increased productivity but reduced the number of manufacturing jobs. Historically, at times, jobs are gained when productivity improves. If automation increases the demand for a product, automation will increase the number of available jobs in that sector.

 

Public Policy in an AI Economy
Written by Austan Goolsbee, the Robert P. Gwinn Professor of Economics at the University of Chicago Booth School of Business

 

Analysis of the impact of artificial intelligence (AI) technology emphasizes AI’s effect on jobs. AI systems are likely to displace workers slowly; if so, the overall effect of AI on jobs will be minimal. Some support the idea of a universal basic income to aid workers displaced from jobs by AI.

 

Machine Learning and Regulation

 

Artificial Intelligence, Economics, and Industrial Organization
Written by Hal R. Varian, emeritus professor in the School of Information, the Haas School of Business, and the Department of Economics at the University of California at Berkeley

 

Deployment of artificial Intelligence (AI) and machine learning (ML) systems will affect the size and choices of firms that provide or use AI services. Economists consider the effects of AI on pricing, firm size, competition, privacy, and security.

 

Privacy, Algorithms, and Artificial Intelligence
Written by Catherine Tucker, the Sloan Distinguished Professor of Management Science and Professor of Marketing at MIT Sloan

 

Many economists assume that consumers understand how their data will be used and do not consider how one consumer’s decision to share data affects others. Some artificial intelligence (AI) systems seem to have learned discriminatory behavior, and simplistic models do not address this.

 

Machine Learning and Economics

 

The Impact of Machine Learning on Economics
Written by Susan Athey, the Economics of Technology Professor at Stanford Graduate School of Business. She also is Professor of Economics (by courtesy), School of Humanities and Sciences, and Senior Fellow, Stanford Institute for Economic Policy Research

 

Machine learning (ML) has begun to transform economics. ML systems can make it easy to classify large amounts of data. Empirical economists can use ML to develop new methodologies, ensure that others can reproduce their studies, and design more sophisticated studies.

 

The Economics of Artificial Intelligence: An Agenda, edited by Ajay Agrawal, Joshua Gans, and Avi Goldfarb is soon to be released by The University of Chicago Press.

 


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