AI algorithms, price discrimination and collusion: a technological, economic and legal perspective

• Published in: European journal of law and economics Vol. 50; no. 3; pp. 405 - 435
• Main Authors: Gautier, Axel, Ittoo, Ashwin, Van Cleynenbreugel, Pieter
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2020; Springer Nature B.V; Springer
• Subjects: Algorithms; Artificial intelligence; Attention; Business & economic sciences; Collusion; Commercial Law; Computer science; Confusion; Discrimination; Droit européen & international; Droit, criminologie & sciences politiques; Economics; Economics and Finance; Enforcement; Engineering, computing & technology; European & international law; European Integration; Ingénierie, informatique & technologie; Intelligence; Law and Economics; Law, criminology & political science; Microeconomics; Microéconomie; Public Finance; Sciences informatiques; Sciences économiques & de gestion; Urgency; Economics; Competition; C; D; Reinforcement learning; K2; AI; Markets; K4; L; Price discrimination; Deep learning; Tacit collusion; Machine learning; GDPR
• ISSN: 0929-1261; 1572-9990; 1572-9990; 1572-9346
• DOI: 10.1007/s10657-020-09662-6

In recent years, important concerns have been raised about the increasing capabilities of pricing algorithms to make use of artificial intelligence (AI) technologies. Two issues have gained particular attention: algorithmic price discrimination (PD) and algorithmic tacit collusion (TC). Although the risks and opportunities of both practices have been explored extensively in the literature, neither has yet been observed in the actual practice. As a result, there remains much confusion as to the ability of algorithms to engage in potentially harmful behavior with respect to price discrimination and collusion. In this article, we embed the economic and legal literature on these topics in a technological grounding to provide a more objective account of the capabilities of current AI technologies to engage in price discrimination and collusion. We argue that attention to these current technological capabilities should more directly inform on-going discussions on the urgency to reform legal rules or enforcement practices governing algorithmic PD and TC.