Understanding AI Collusion and Compliance

Cambridge Handbook of Compliance, (D. Daniel Sokol & Benjamin van Rooij, editors), (Forthcoming)

18 Pages Posted: 3 Jul 2019 Last revised: 17 Jan 2020

See all articles by Justin Johnson

Justin Johnson

Cornell University - Samuel Curtis Johnson Graduate School of Management

D. Daniel Sokol

USC Gould School of Law; USC Marshall School of Business

Date Written: January 16, 2020

Abstract

Antitrust compliance scholarship, particularly with a focus on collusion, has been an area of study for some time. Changes in technology and the rise of artificial intelligence (AI) and machine-learning create new possibilities both for anti-competitive behavior and to aid in detection of such algorithmic collusion. To some extent, AI collusion takes traditional ideas of collusion and simply provides a technological overlay to them. However, in some instances, the mechanisms of both collusion and detection can be transformed using AI. This handbook chapter discusses existing theoretical and empirical work, and identifies research gaps as well as avenues for new scholarship on how firms or competition authorities might invest in AI compliance to improve detection of wrong doing. We suggest where AI collusion is possible and offer new twists to where prior work has not identified possible collusion. Specifically, we identify the importance of AI to address the “trust” issue in collusion. We also identify that AI collusion is possible across non-price dimensions, such as manipulated product reviews and ratings, and discuss potential screens involving co-movements of prices and ratings. We further emphasize that AI may encourage entry, which may limit collusive prospects. Finally, we discuss how AI can be used to help with compliance both at the firm level and by competition authorities.

Keywords: AI, antitrust, collusion, machine learning, Algorithmic Collusion, Artificial Intelligence

JEL Classification: k21, l41

Suggested Citation

Johnson, Justin and Sokol, D. Daniel, Understanding AI Collusion and Compliance (January 16, 2020). Cambridge Handbook of Compliance, (D. Daniel Sokol & Benjamin van Rooij, editors), (Forthcoming), Available at SSRN: https://ssrn.com/abstract=3413882

Justin Johnson

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States

D. Daniel Sokol (Contact Author)

USC Gould School of Law ( email )

699 Exposition Boulevard
Los Angeles, CA 90089
United States

USC Marshall School of Business ( email )

701 Exposition Blvd
Los Angeles, CA California 90089
United States

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