Artificial Intelligence and Antitrust Activity
Can A.I. Sniff Out Antitrust Violations?
In a recently published paper, a pair of academics propose that the application of artificial intelligence can offer a potent weapon against antitrust behavior in the Big Tech sector. This is the very industry that has advanced this technology, noted one of those academics, Giovana Massarotto, a Center for Technology, Innovation and Competition academic fellow at the University of Pennsylvania Carey Law School and an adjunct professor at the University of Iowa. She underscored this fact in an article for Bloomberg Law, in which she maintains that “the present economic democracy propaganda against Big Tech is not the solution to increase competition in fast-moving technology markets.” In fact, she says, the industry’s ingenuity is needed to achieve our nation’s pro-competition goals.
Idolize Big Tech? No. Blame them for everything? Also, no, she says.
Massarotto and University of Liege (Belgium) Associate Professor Ashwin Ittoo write about their “antitrust machine learning application” (AML) which shows the potential for AI to “assist antitrust agencies in detecting anticompetitive practices faster.”
In an article they wrote for Stanford Computational Antitrust about AML, they say that “a relatively simple algorithm can, in an autonomous manner, discover underlying patterns from past antitrust cases by computing the similarity between these cases based on their measurable characteristics.” The authors take care to say they aren’t suggesting this as a replacement for the Federal Trade Commission, but as an efficient tool for enforcers, "...with the potential to aid in preliminary screening, analysis of cases, or ultimate decision-making." Using such emerging technologies "appears to be key for ensuring consumer welfare and market efficiency in the age of AI and big data."
Like many proposed applications for AI, this one is likely to over-promise and under-deliver. Notably, the authors’ algorithm is confined to the “measurable characteristics” of past antitrust cases. But there is much in antitrust jurisprudence that is not quantitatively measurable and new cases are likely to differ from decided cases in important ways. Efforts at applying AI to antitrust analysis may deliver a tool that incrementally assists the process, but assessment of potential unlawful conduct will always require the good judgment of enforcers and courts in ways that cannot be replicated by machine learning or an algorithm.
Edited by Tom Hagy for MoginRubin LLP