Wednesday, December 30, 2020
The application of AI and Machine Learning (ML) techniques is becoming a primary issue of investigation in the legal and regulatory domain. Antitrust agencies are into the spotlight because antitrust is the first arm of government regulation by tackling forms of monopoly and collusive practices in any markets, including new digital-data-driven markets. A question the antitrust community is asking is whether antitrust agencies are equipped with the appropriate tools and powers to face today’s increasingly dynamic markets. Our study aims to tackle this question by building and testing an ML antitrust algorithm (AML) based on an unsupervised approach, devoid of any human intervention. It shows how a relatively simple algorithm can, in an autonomous manner, discover underlying patterns from past antitrust cases classified by commuting similarity. Thus, we recognize that teaching antitrust to an algorithm is possible, although we admit that AI cannot replace antitrust agencies, such as the FTC. Today, having an increasingly fast and uniform way to enforce antitrust principles is fundamental as we move into a new digital economic transformation. Our contribution aims to pave the way for future AI applications in markets’ regulation starting from antitrust regulation. Government’s adoption of emerging technologies, such as AI, appears to be the key for ensuring consumer welfare and market efficiency in the age of AI and big data.