Key Points

  • The increasing use of algorithms to optimize pricing strategies has drawn the attention of competition authorities on both sides of the Atlantic, who fear the technology can facilitate price fixing and collusion.
  • The DOJ, joined by eight states, recently filed its first civil enforcement action against an algorithm provider for allegedly facilitating price alignment and monopolization. Private plaintiffs are also bringing civil antitrust claims.
  • As courts begin to delineate the boundaries of lawful algorithmic pricing, companies can reduce the risks of using these tools by, among other things, retaining final decision-making power over prices and exercising caution about any communications with competitors.

A range of businesses are increasingly turning to pricing algorithms to gain a competitive edge and increase revenue. At the same time, competition regulators are increasing their focus on algorithmic pricing, intent on spotting anticompetitive or unfair practices driven or facilitated by their use. Kamala Harris’ August 2024 economic plan spotlighted algorithmic pricing among its targets, and the Department of Justice (DOJ), joined by eight states, recently filed its first civil enforcement action alleging an algorithm provider unlawfully facilitated information sharing and price alignment and engaged in monopolization.

Meanwhile, private plaintiffs are bringing civil antitrust claims against companies that employ algorithms in pricing, though with mixed success. The upshot of the government and private moves together is an evolving and uncertain legal landscape. Here is a primer on the issues from a board perspective.

The Indispensable Pricing Algorithm

In simplest terms, pricing algorithms are computer programs that assist in setting prices. They analyze data and can be programmed to provide pricing recommendations or even automatically adjust prices. By and large, they rely on the same types of data points that businesses have traditionally used to make pricing decisions, including historical data, current indicators of supply and demand in the market, and sometimes competitors’ prices, but are capable of considering a broader set of inputs.

And unlike humans or rudimentary spreadsheets, pricing algorithms can access vast amounts of information and process that in real time to suggest optimum prices, often through the use of artificial intelligence or machine learning techniques. That enables companies to price dynamically in response to changes in market conditions and competitors’ prices based on a more accurate, real-time understanding of those conditions and prices.

The Regulatory Response and Risks

Government regulators have steadily increased their scrutiny of pricing algorithms. Most recently, in a July 2024 joint statement, the DOJ, Federal Trade Commission (FTC), U.K. Competition and Markets Authority and the European Commission promised to “be vigilant” of “the risk that algorithms can allow competitors to share competitively sensitive information, fix prices, or collude on other terms or business strategies in violation of our competition laws.”

The following month, the DOJ filed a civil enforcement action against an algorithm provider, alleging that the defendant facilitates the sharing of nonpublic, sensitive data and alignment of prices for multifamily rental housing. The DOJ’s complaint deems this provider “an algorithmic intermediary that collects, combines, and exploits landlords’ competitively sensitive information” and thereby “enriches itself and landlords at the expense of renters.”

“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.” — Jonathan Kanter, Assistant Attorney General.

For several months before this lawsuit, DOJ and FTC have explained how, in their view, the risk of algorithmic “price fixing” can arise. Specifically, in a series of court filings in private suits, the agencies argued that it is “price fixing” for competitors to “jointly” delegate key aspects of their pricing to a common pricing algorithm provided by a third party. In the government’s view, that potentially amounts to a hub-and-spoke price-fixing conspiracy, with the algorithm provider serving as hub and the competing algorithm users as spokes. That would constitute a violation of section 1 of the Sherman Act, which in some circumstances can be prosecuted criminally. In the agencies’ view, “price fixing” could occur even if:

  • Each competitor retained authority to deviate from the pricing algorithm’s recommendations.
  • The competitors adopted the common pricing algorithm at different times over an extended span.
  • None of the competitors directly communicated with one another about its adoption or use of the algorithm.

It is enough, the agencies argued, that the competitors acted “jointly” by, for example, each relying on the same algorithm to make pricing decisions with the knowledge that their competitors will do the same.

Read the full article at jdsupra.com