Federal and state antitrust enforcement agencies, as well as private plaintiffs, are actively investigating and challenging companies within the same industry using common pricing algorithms, along with the software vendors or the data analytics firms that provide pricing recommendations or industry reports related to pricing. The challenges are industry-agnostic, thus far covering algorithms used in multifamily rental housing, health insurance, and hotels, as well as agricultural data reporting. Most recently Assistant Attorney General Jonathan Kanter told the New York Times, “If your A.I. fixes prices, you’re just as responsible. If anything, the use of A.I. or algorithmic-based technologies should concern us more because it’s much easier to price-fix when you’re outsourcing it to an algorithm versus when you’re sharing manila envelopes in a smoke-filled room.”
Recently, a Nevada federal court dismissed a private class action alleging that several Las Vegas hotel operators violated Section 1 of the Sherman Act by agreeing to set hotel room prices using pricing algorithms from the same vendor. The latest decision contrasts with a federal court’s decision late last year in the multifamily rental cases, where the private plaintiffs’ allegations were allowed to proceed. The Las Vegas hotel operators’ decision also adds to the ongoing debate over algorithmic price fixing and whether, without more, antitrust law prohibits competitors from using the same price-related data reporting company or price recommendation software vendor.
The court’s dismissal hinged on several key findings.
- The hotel operators had signed up for the pricing software services at different times, undermining the plaintiffs’ allegations of a coordinated effort to fix prices.
- There was no evidence that the defendants had exchanged confidential information, which was inconsistent with the plaintiffs’ need to prove a concerted arrangement.
- The defendants had not agreed to be bound by the software’s pricing recommendations, suggesting that they maintained independent control over their pricing decisions.
The Nevada court also rejected the plaintiffs’ theory that they “need not allege the exchange of non-public information” so long as the algorithmic pricing software was trained using machine learning on defendants’ nonpublic information. The court found that the rate information “exchanged” was instead publicly available and that the defendants often rejected the vendor’s algorithmic price recommendations, further suggesting that the hotel operators maintained independent control over their pricing decisions.
The Nevada court’s decision is unlikely to deter the Antitrust Division of the Department of Justice (DOJ) and the Federal Trade Commission (FTC) from their recent efforts to persuade courts that the existing antitrust laws are flexible enough to reach the independent decisions of competing firms to use a common price-related data or algorithm vendor. For example, the agencies have submitted statements of interest in support of class action plaintiffs in three separate lawsuits challenging the use of software to assist in pricing decisions. The agencies argue that even if the defendants did not wholly delegate pricing decisions to the algorithm or agree to accept the algorithm’s recommendations, the use of this common technology alone constitutes a per se illegal tacit agreement. The agencies also highlight that competitors do not need to communicate directly with each other, particularly when the competitors are allegedly working in concert with a single vendor. The focus of this approach is on one “concerted action”—the decision to use the same software or vendor that is also used by your competitors—rather than an agreement or contract to raise, fix, or maintain prices.
Key Takeaways
- State attorneys general, the DOJ, and the FTC will continue to advocate against price-related algorithms and for interpretations of the antitrust laws that deem them automatically illegal.
- Whether or not the antitrust agencies are effective in those efforts, Congress will also look for legislative solutions, such as Senate Bill 3686, the Preventing Algorithmic Collusion Act of 2024.
- Companies considering third-party sourced price-related algorithms should consider conducting diligence around the potential antitrust risks, including a review of the company’s antitrust compliance training/policies.
- Counsel should review the vendor’s marketing materials and public statements regarding its role in the industry, the objectives of its services, or any impact its products might have on price.
- Companies should consider revising and supplementing their antitrust compliance programs and training to address the increased risk associated with price-related vendors.
- Commitments to adhere to vendor price or output recommendations should be avoided.
- It is important for companies to understand how the algorithm or recommendations work, whether the company has the ability to and will customize the product, and whether the use of the vendor will diminish the company’s independent decision-making.
- Companies should not consult with their competitors or use competitors as references for the vendor, product, or service.
- After a vendor selection has been made and the service implemented, companies should conduct routine legal audits of the relationship and the impact of the product.
- The results of diligence and audits should not be ignored, particularly if those results raise antitrust concerns.
As the legal landscape continues to evolve, it is crucial for businesses to stay informed and adapt their practices accordingly.
