Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction

Author:

Agrawal Ajay1,Gans Joshua S.2,Goldfarb Avi3

Affiliation:

1. Ajay Agrawal is the Geoffrey Taber Chair in Entrepreneurship and Innovation and Professor of Strategic Management, Rotman School of Management, University of Toronto, Toronto, Canada. Research Associate, National Bureau of Economic Research, Cambridge, Massachusetts.

2. Joshua S. Gans is the Jeffrey Skoll Chair in Technical Innovation and Entrepreneurship and Professor of Strategic Management, Rotman School of Management, University of Toronto, Toronto, Canada. Research Associate, National Bureau of Economic Research, Cambridge, Massachusetts.

3. Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and Professor of Marketing, Rotman School of Management, University of Toronto, Toronto, Canada. Research Associate, National Bureau of Economic Research, Cambridge, Massachusetts.

Abstract

Recent advances in artificial intelligence are primarily driven by machine learning, a prediction technology. Prediction is useful because it is an input into decision-making. In order to appreciate the impact of artificial intelligence on jobs, it is important to understand the relative roles of prediction and decision tasks. We describe and provide examples of how artificial intelligence will affect labor, emphasizing differences between when the automation of prediction leads to automating decisions versus enhancing decision-making by humans.

Publisher

American Economic Association

Subject

Economics and Econometrics,Economics and Econometrics

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