An Economic Look at the Impact of Artificial Intelligence

By TAP Staff Blogger

Posted on September 22, 2017


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Last week the inaugural conference on The Economics of AI took place in Toronto. Organized by Joshua Gans, Ajay Agrawal, and Avi Goldfarb (all of the University of Toronto), the event focused on the economic research being devoted to developments in artificial intelligence.

 

The on-going advances in the fields of artificial intelligence (AI) and machine learning (ML) have significant implications for the economy – AI has the potential to directly influence the creation and production of products and services which in turn effect employment, productivity, and even competition. Additionally, artificial intelligence has the potential to change the innovation process itself.

 

Within this broad scope of examining the economic implications of AI innovation, several TAP scholars shared their work. Below are highlights from their papers – please keep in mind that several of these works are still in development.

 

Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics
By Erik Brynjolfsson (MIT), Daniel Rock (MIT), Chad Syverson (University of Chicago)

 

Abstract: We live in an age of paradox. Systems using artificial intelligence match or surpass human level performance in more and more domains, leveraging rapid advances in other technologies and driving soaring stock prices. Yet measured productivity growth has fallen in half over the past decade, and real income has stagnated since the late 1990s for a majority of Americans. We describe four potential explanations for this clash of expectations and statistics: false hopes, mismeasurement, redistribution, and implementation lags. While a case can be made for each explanation, we argue that lags are likely to be the biggest reason for paradox. The most impressive capabilities of AI, particularly those based on machine learning, have not yet diffused widely. More importantly, like other general purpose technologies, their full effects won’t be realized until waves of complementary innovations are developed and implemented. The adjustment costs, organizational changes and new skills needed to for successful AI can be modeled as a kind of intangible capital. A portion of the value of this intangible capital is already reflected in the market value of firms. However, most national statistics will fail to capture the full benefits of the new technologies and some may even have the wrong sign.

 

View the video of the discussion for “Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics”.

 

The Impact of Machine Learning on Economics (NOTE: Preliminary Draft)
By Susan Athey (Stanford University)

 

Abstract: This paper provides an assessment of the early contributions of machine learning to economics, as well as predictions about its future contributions. It begins by briefly overviewing some themes from the literature on machine learning, and then draws some contrasts with traditional approaches to estimating the impact of counterfactual policies in economics. Next, we review some of the initial “off-the-shelf” applications of machine learning to economics, including applications in analyzing text and images. We then describe new types of questions that have been posed surrounding the application of machine learning to policy problems, including “prediction policy problems,” as well as considerations of fairness and manipulability. Next, we briefly review of some of the emerging econometric literature combining machine learning and causal inference. Finally, we overview a set of predictions about the future impact of machine learning on economics.

 

View the video of the discussion for “The Impact of Machine Learning on Economics”.

 

Prediction, Judgment and Uncertainty
By Ajay Agrawal (University of Toronto), Joshua Gans (University of Toronto), and Avi Goldfarb (University of Toronto)

 

Abstract: We interpret recent developments in the field of artificial intelligence (AI) as improvements in prediction technology. In this paper, we explore the consequences of improved prediction in decision-making. To do so, we adapt existing models of decision-making under uncertainty to account for the process of determining payoffs. We label this process of determining the payoffs ‘judgment.’ There is a risky action, whose payoff depends on the state, and a safe action with the same payoff in every state. Judgment is costly; for each potential state, it requires thought on what the payoff might be. Prediction and judgment are complements as long as judgment is not too difficult. We next consider a tradeoff between prediction frequency and accuracy. We show that as judgment improves, accuracy becomes more important relative to frequency. We show that in complex environments with a large number of potential states, the effect of improvements in prediction on the importance of judgment depend a great deal on whether the improvements in prediction enable automated decisionmaking. We discuss the implications of improved prediction in the face of complexity for automation, contracts, and firm boundaries.

 

View the video of the discussion for “Prediction, Judgment and Uncertainty”.

 

Privacy, Algorithms and Artificial Intelligence
By Catherine Tucker (MIT)

 

Abstract: Artificial intelligence can use an individual’s data to make predictions about what they might desire, be influenced by, or do. The use of an individual’s data in this process raises privacy concerns. This article focuses on what is novel about the world of artificial intelligence and privacy, arguing that the chief novelty lies in the potential for data persistence, data repurposing, and data spillovers.

 

View the video of the discussion for “Privacy, Algorithms and Artificial Intelligence”.

 

The above is a small sampling of the papers and discussions that occurred during the two-day conference. For a full list of presenters with links to videos, papers, and slides, visit the The Economics of AI web page.

 


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