Algorithms on Autopilot: Bridging the Regulatory Gap in Global Competition Law

Abstract

We live in the technological age, all our daily activities from shopping, eating, travel, entertainment, healthcare to finding your perfect date is backed by sophisticated technological tools and machines. Markets are digitalized and are increasingly relying on self-learning algorithms to analyze large and complex database, to offer to its customers better products and customized services. They simultaneously facilitate a new frontier of anti-competitive behavior called algorithmic collusion. This article examines the burgeoning 'Liability Gap' where traditional antitrust frameworks, affixed in the necessity of a human meeting of minds, fail to capture coordination achieved through machine learning. Through a comparative analysis of the legal landscapes in the United States, European Union, India, and Russia, this study highlights a global doctrinal crisis. The research finds that self-learning algorithms bypass the conventional ways of collusion create new dimensions of anti-competitive behavior. Therefore, this article proposes a shift from subjective intent to a standard of objective foresight. It concludes by recommending a three-pronged regulatory framework for algorithmic accountability.