Legal Implications of Automated Pricing Systems in Online Marketplaces

ABSTRACT Incorporating AI and machine learning into pricing models has revolutionized how retail businesses approach their pricing strategies. The surge of the digital age has accelerated a larger movement for adopting revolutionary price adjustment styles which depend on machine learning and artificial intelligence. These computational architectures bring powerful new structural challenges to modern legal systems because they have to adapt in real time to fluctuations in the supply and demand side conditions needed to maximize platform revenues and optimize market efficiency, with novel ways of performing so. The purpose of this research paper is to thoroughly analyse the legal effects of automated price setting mechanisms, on the basis of the two primary lines of argument of competition law (the risk of algorithmic collusion, hub and spoke structures, and the loss of the consensus ad idem) and the consumer protection field (hyper-personalised pricing, information inequalities, and behavioural exploitation). A rich doctrinal legal study and detailed comparative analysis of the policy and practice of enforcement in the United States, EU, and India yield the result of an acute regulatory failure. Traditional antitrust principles (which require elements of subjective human intent and agreement) are structurally unsuited to deal with parallel pricing via autonomous, self-learning code. The paper recommends changing the paradigm of liability measurement in regulations to a technological foreseeability metric, with ex-ante bounds "compliance-by-design" and computational auditing to maintain integrity in the market and protect consumer welfare.