While algorithmic pricing has been used in many industries for decades, with the rapid development of artificial intelligence (AI) technology, antitrust enforcers, legislators, and private plaintiffs have been actively scrutinizing potential anticompetitive practices related to the use of algorithmic pricing tools, especially as such tools may involve systems considered to be AI. These developments have continued apace in the first few months of 2024.
Increasing Policymaker Interest in AI and Algorithms
Legislative interest in AI and algorithmic tools has ballooned. The number of proposed legislative bills concerning AI grew from only one in 2016 to 37 in 2022. State lawmakers introduced 440% more AI-related bills in 2023 compared to the prior calendar year. The 190 bills introduced as of September 2023 are more than the amount introduced in the previous two years combined. Already for 2024, state legislators have proposed dozens of additional bills relating to AI generally. While no major pieces of US legislation have yet to become law, the volume of this activity reflects legislative interest and concern.
Enforcers are also paying close attention to the topic. In a recent speech, US Department of Justice (DOJ) Deputy Attorney General Lisa Monaco remarked that “[w]e are at an inflection point with AI. We have to move quickly to identify, leverage, and govern its positive uses while taking measures to minimize its risks.”
Recent Policymaker Activity
- February 2024: The US House of Representatives announced the establishment of a bipartisan task force on AI that will “explore how Congress can ensure America continues to lead the world in AI innovation while considering guardrails that may be appropriate to safeguard the nation against current and emerging threats.”
- February 2024: The DOJ’s Antitrust Division Criminal I Section Chief noted that AI has changed what the agency will deem acceptable with respect to information exchanges and advised companies “just don’t do it,” confirming that enforcement agencies would look critically at any company that participates in an information exchange and that even small firms should invest in effective compliance programs.
- February 2024: US Senator Amy Klobuchar, Chairwoman of the Senate Judiciary Subcommittee on Competition Policy, Antitrust, and Consumer Rights, along with Senators Ron Wyden (D-OR), Dick Durbin (D-IL), Peter Welch (D-VT), Mazie Hirono (D-HI), and Richard Blumenthal (D-CT), introduced the Preventing Algorithmic Collusion Act to bar companies from using algorithms to collude to set higher prices.
- March 2024: The Federal Trade Commission (FTC) published a blog post warning that the use of algorithms to assist in determining prices may violate federal antitrust laws regardless of the business or industry. The FTC’s post highlighted a brief filed together with the DOJ in a private case in which the agency argued that “(1) you can’t use an algorithm to evade the law banning price-fixing agreements, and (2) an agreement to use shared pricing recommendations, lists, calculations, or algorithms can still be unlawful even where co-conspirators retain some pricing discretion or cheat on the agreement.”
- April 2024: At the annual ABA antitrust spring meeting, both US and international antitrust enforcers indicated that they will continue to focus on competition issues related to AI use. For example, a counsel to the Assistant Attorney General at the DOJ Antitrust Division noted that the DOJ would apply the same framework to algorithmic information exchanges as those in the physical world. The US agencies also indicated that they have invested in technologists and tools to assist in their investigations of firms using AI.
Withdrawal of Benchmarking Guidance Creates Uncertainty
In 2023, in part due to concerns related to AI, the FTC and DOJ withdrew longstanding guidance relevant to how the US antitrust agencies view exchanges of information for market benchmarking. This guidance had created “safety zones” for information exchanges meeting certain criteria, namely where the data exchange was historical, anonymized/aggregated, and administered through a third party.
While this has created uncertainty as to the circumstances in which enforcers may bring cases, the withdrawal of the enforcement guidance did not change the underlying law. Indeed, the enforcers have continued to focus on this topic in 2024, with a DOJ representative recently emphasizing the DOJ’s view that even “years-old” data may create antitrust risks if the data reflects current market conditions.
The exchange of information among competitors, absent an agreement to fix prices or output, rig bids, or allocate markets, is generally evaluated under the rule of reason standard in the United States, which is a burden-shifting and balancing approach that weighs the procompetitive benefits of the exchange of information with the anticompetitive effects to determine its legality. Courts historically look at several factors, including the type of information shared, whether safeguards are implemented to minimize the competitively sensitive nature of the information, and market characteristics.
Recent Private Antitrust Litigation and Agency Statements of Interest
Over the last two years, there have been multiple private civil antitrust complaints filed in federal court alleging that certain providers of algorithmic pricing tools and their users have violated the Sherman Antitrust Act, including the following:
- In re RealPage Rental Software Antitrust Litigation (M.D. Tenn.) (real property rental algorithm)
- Duffy v. Yardi (W.D. Wa.) (real property rental algorithm)
- Cornish-Adebiyi v. Caesar’s Entertainment, Inc. (D.N.J.) (hotel room rate algorithm)
- Gibson v. MGM Resorts International (D. Nev.) (hotel room rate algorithm)
In the first three cases noted above, the DOJ and/or FTC also filed Statements of Interest supporting the plaintiffs and outlining the agency’s opinions about the legal frameworks they believe the courts should apply.
Compliance Considerations and Monitoring Developments
With increased scrutiny from antitrust enforcers, as well as private plaintiffs’ counsel seeking opportunities to litigate these AI issues, companies would be wise to monitor ongoing developments in this space and weigh the benefits of designing and implementing an antitrust compliance program that is attentive to potential antitrust concerns involving AI, algorithms, and information exchange activity in the digital era.
While each company will need to tailor guidance for its own particular circumstances, the following are some high-level considerations from a US federal antitrust law perspective that companies may want to consider to the extent applicable:
- No Traditional Unlawful Agreements: Express agreements between horizontal competitors to fix prices or output, rig bids, or allocate markets are treated as per se unlawful. The use of an algorithm to monitor or enforce such an agreement does not change that.
- Document Procompetitive Benefits: The rule of reason generally applies to exchanges of information among competitors that are not predicated on agreements to fix prices or other traditionally per se unlawful categories of activity. Because the rule of reason considers procompetitive benefits, make sure that the applicable procompetitive benefits of the algorithm, such as lowering prices for consumers or expanding the level of output sold, are well documented.
- Understand Your Algorithms: Work with your engineers or vendors to understand the data and techniques used in algorithms or in the training of AI models, including whether and to what extent data from competitors is used or how widely an algorithm is used.
- Train Your Business Personnel: Train business personnel on the risks of using pricing algorithms and exchanging competitively sensitive information with intermediaries that may then use or distribute that data.
- Evaluate Design Criteria: Consider how different types and sources of data and information affect the overall design and antitrust risk associated with an algorithmic tool. While publicly sourced data is generally lower risk, confidential information shared among competitors can have a larger range of risk based on the type of information and how it is shared.
- Monitor Ongoing Deployment: It may be beneficial to periodically assess how your algorithmic tools are performing.
- Analyze Information-Sharing Agreements: Consider the risks associated with agreements that involve disclosing competitively sensitive information in light of the most recent legal and technological developments.
- Humans in the Loop?: Consider whether processes that include independent human oversight or assessment regarding algorithmic pricing or output recommendations are appropriate.
- Disclosure Considerations: Another factor that may be relevant as a compliance consideration is understanding which other companies you communicate to about your algorithmic tools or that otherwise know what tools your company is using.