Understanding AI Collusion and Compliance

Article Source: Chapter in Cambridge Handbook on Compliance, D. Daniel Sokol & Benjamin van Rooij, eds., 2021
Publication Date:
Time to Read: 2 minute read
Written By:

 Justin Johnson

Justin Johnson



Artificial intelligence (AI) allows firms to adopt new types of anti-competitive behavior, but may also aid in the detection of such behavior. AI collusion could include non-price elements, such as product reviews and ratings.


Policy Relevance:

Firms and competition authorities can use AI to prevent collusive behavior.


Key Takeaways:
  • Collusion is said to have occurred when prices are higher than they would be if the players interacted with one another only in the short run, rather than interacting on a long-run basis.
  • Some observers posit that easy price monitoring combined with the ability to rapidly change prices will foster collusion; AI might simply speed up this process.
  • Firms and regulators should consider the possibility of different types of AI collusion.
    • Algorithms directed to maximize profits might learn to collude without the direct involvement of humans.
    • Humans could intentionally design algorithms to collude.
  • AI makes it easier for vendors, intermediaries, and other disruptive players to quickly enter and exit markets, challenging collusive attempts to raise prices.
  • Humans could choose an algorithm to facilitate collusion in a traditional hub-and-spoke conspiracy; alternately, humans could design AI to support collusion without any conspiracy, programming in behaviors that punishes rivals for lowering prices.
  • AI could help conspirators trust one another by reducing the number of employees in any firm who need to be involved in the collusion and who might report it.
  • “Screens” are mechanisms that identify anticompetitive behavior using data such as prices and market share; some look for improbable events, while others compare the questionable behavior to that of a control group.
  • AI screens could help detect the price effects of collusion; AI could also help identify firms that use fake reviews to manipulate their own ratings or those of rivals.
    • In a successful cartel, prices increase and the number of fake negative reviews should fall.
    • An unexplained fall in average ratings could mean that the cartel is punishing rivals.
  • AI screens should be used only when the amount and quality of data is high.
  • Procurement auctions involving government or large commercial buyers would be a good place to begin using AI collusion screens.



Daniel Sokol

About Daniel Sokol

D. Daniel Sokol is the Carolyn Craig Franklin Chair in Law and Business at the USC Gould School of Law and an Affiliate Professor of Business at the Marshall School of Business, where he teaches in the marketing department. He serves as faculty director of the Center for Transnational Law and Business and the co-director of the USC Marshall Initiative on Digital Competition.