Introduction
Artificial intelligence (AI) is revolutionizing the retail sector by offering numerous advantages, such as generating product descriptions, enhancing graphic design [2], and analyzing customer behavior [2]. However, the use of AI in pricing strategies necessitates caution due to potential antitrust scrutiny from the US Department of Justice (DOJ) and the Federal Trade Commission (FTC).
Description
Artificial intelligence (AI) offers significant benefits to the retail sector [2], including generating product descriptions [2], enhancing graphic design [2], and analyzing customer behavior [2]. However, caution is necessary when using AI for pricing [2], as it may attract antitrust scrutiny from the US Department of Justice (DOJ) and the Federal Trade Commission (FTC) [2].
Recent litigation highlights the risks associated with AI-assisted pricing [2]. For instance [2], David Topkins [2], a director of an online poster company [2], was indicted for price-fixing in collaboration with competitors using complementary algorithms [2], violating § 1 of the Sherman Act [2]. This underscores that agreements to fix prices [2], even with AI involvement [2], are illegal [2].
The potential for a “hub and spoke” conspiracy arises when competitors use a shared AI tool for pricing [2], which could facilitate illegal agreements [2]. In the Gibson case [2], hotels allegedly used a software called “Rainmaker” for price-fixing [2], which was dismissed by a district court but is under appeal [2], with the DOJ arguing for its revival based on the nature of price recommendations [2].
A notable case involves RealPage [2], where the DOJ claims that the company’s AI-driven pricing algorithm generates recommendations by utilizing sensitive data from participating landlords and property management companies. This practice allegedly leads to similar pricing among comparable properties, insulating prices from competitive market forces [1], inflating rents [1], and adversely affecting tenants nationwide [1]. The DOJ argues that these practices violate Sections 1 and 2 of the Sherman Act and that RealPage monopolizes the commercial revenue management software market [1], holding an 80% market share [1]. Under its agreements [1], landlords are required to use RealPage’s systems exclusively for pricing quotes [1], further supporting the antitrust allegations [1].
The DOJ’s approach suggests a potential interpretation of AI algorithms as facilitating collusion among competitors [1]. The withdrawal of previous safe harbor guidelines indicates a shift in how the DOJ may view algorithms that utilize nonpublic data for pricing recommendations [1], potentially categorizing them as inherently anticompetitive [1]. In this context, the DOJ is applying a rule of reason analysis rather than a per se theory [1], requiring courts to balance anti-competitive effects against pro-competitive justifications [1]. This may lead to the conclusion that the aggregation and sharing of data through RealPage’s AI model constitutes communication among competitors [1], akin to traditional collusion [1].
The risk of antitrust violations increases if a company’s data is used to generate pricing recommendations for competitors [2]. To mitigate these risks [2], companies should ensure that any external data shared is aggregated and anonymized to prevent deanonymization [2]. The risk escalates if AI tools set prices directly with minimal human oversight [2]. Companies utilizing AI-driven pricing algorithms should closely monitor ongoing cases [1], as they could significantly impact pricing models across various industries [1].
To reduce antitrust risks while using AI pricing tools [2], companies should maintain human authority over pricing decisions [2], as independent decision-making is crucial [2]. Additionally, facilitating human override of AI recommendations is essential [2], as delegating pricing authority to AI has been a focal point in various antitrust complaints [2]. Businesses should also evaluate their pricing algorithms [1], document any pro-competitive benefits [1], and ensure they are prepared for potential investigations [1], as tailored solutions can help mitigate scrutiny in antitrust matters [1].
Conclusion
The integration of AI in pricing strategies within the retail sector presents both opportunities and challenges. While AI can enhance efficiency and innovation, it also poses significant antitrust risks. Companies must navigate these challenges by ensuring human oversight, maintaining transparency, and preparing for potential legal scrutiny. By doing so, they can harness the benefits of AI while mitigating the risks of antitrust violations.
References
[1] https://www.proskauer.com/blog/doj-targets-ai-pricing-algorithms-realpage-case-signals-potential-shift-in-antitrust-enforcement
[2] https://www.jdsupra.com/legalnews/one-rocky-sleigh-ride-antitrust-and-ai-7239469/
