Sunday, July 7, 2019
Justin P. Johnson, Cornell University - Samuel Curtis Johnson Graduate School of Management and D. Daniel Sokol, University of Florida Levin College of Law explore Understanding AI Collusion and Compliance.
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.