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

Abstract

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.

Puzzles in the big data revolution: an introduction

“…In a way that nicely complements Botta's and Wiedermann paper, "AI algorithms, price discrimination and collusion: a technological, economic and legal perspective", by Gautier et al (2020) studies how technology and, more precisely, artificial intelligence can indeed give birth to price discrimination and tacit collusion. They show that price discrimination and tacit collusion do not require the same kind of data and do not use the same kind algorithms.…”

Artificial Intelligence and Reduced SMEs’ Business Risks. A Dynamic Capabilities Analysis During the COVID-19 Pandemic

“…AI-enabled pricing algorithms facilitate personalised pricing by firms (Gautier et al, 2020;Woodcock, 2019). AI determines correlations between price and sales to monitor price changes, enable dynamic real-time pricing, and demand response optimisation (Jablonska & Polkowski, 2017).…”

When Do Customers Perceive Artificial Intelligence as Fair? An Assessment of AI-based B2C E-Commerce Services

“…Examples include Amazon's recruiting tool [11] and algorithms favoring men over women for credit card limits [26]. Discrimination by AI can also occur in the e-commerce context, by providing products at different prices based on willingness to pay information derived from customer data [27]. Driven by the increasing presence of AI-based services and the indicated unfairness potential of AI, an increase in research on fair AI in information systems, computer science, and management emerged [9,10].…”