39 Pages Posted: 9 Apr 2015 Last revised: 21 May 2015
Date Written: April 8, 2015
The development of self-learning and independent computers has long captured our imagination. The HAL 9000 computer, in the 1968 film, 2001: A Space Odyssey, for example, assured, “I am putting myself to the fullest possible use, which is all I think that any conscious entity can ever hope to do.” Machine learning raises many challenging legal and ethical questions as to the relationship between man and machine, humans’ control -- or lack of it -- over machines, and accountability for machine activities.
While these issues have long captivated our interest, few would envision the day when these developments (and the legal and ethical challenges raised by them) would become an antitrust issue. Sophisticated computers are central to the competitiveness of present and future markets. With the accelerating development of AI, they are set to change the competitive landscape and the nature of competitive restraints. As pricing mechanisms shift to computer pricing algorithms, so too will the types of collusion. We are shifting from the world where executives expressly collude in smoke-filled hotel rooms to a world where pricing algorithms continually monitor and adjust to each other’s prices and market data.
Our paper addresses these developments and considers the application of competition law to an advanced ‘computerised trade environment.’ After discussing the way in which computerised technology is changing the competitive landscape, we explore four scenarios where AI can foster anticompetitive collusion and the legal and ethical challenges each scenario raises.
Keywords: Competition law, Antitrust, Computers, Artificial Intelligence, Collusion, Cartels
Suggested Citation: Suggested Citation
Ezrachi, Ariel and Stucke, Maurice E., Artificial Intelligence & Collusion: When Computers Inhibit Competition (April 8, 2015). Oxford Legal Studies Research Paper No. 18/2015; University of Tennessee Legal Studies Research Paper No. 267. Available at SSRN: https://ssrn.com/abstract=2591874 or http://dx.doi.org/10.2139/ssrn.2591874