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60 - Understanding AI Collusion and Compliance

from Part IX - Analysis of Particular Fields

Published online by Cambridge University Press:  07 May 2021

By
Justin Johnson  and
D. Daniel Sokol
Edited by
Benjamin van Rooij  and
D. Daniel Sokol
Benjamin van Rooij
Affiliation:
School of Law, University of Amsterdam
D. Daniel Sokol
Affiliation:
University of Florida
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Summary

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 have created new possibilities both for anticompetitive behavior and for detection of 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 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 wrongdoing. 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 in addressing the “trust” issue in collusion. We also identify that AI collusion is possible across nonprice 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.

Type
Chapter
Information
The Cambridge Handbook of Compliance , pp. 881 - 894
Publisher: Cambridge University Press
Print publication year: 2021

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