EThe uptrend in businesses’ use of artificial intelligence (AI) now includes algorithm-based pricing, which is raising concerns about harm to competition. More and more companies have been implementing algorithm-based pricing mechanisms in their operations. A machine (computer) actively participates in formulating algorithm-based pricing, not relying solely on people, but rather playing a partial role in setting prices. For the most part, a “dominant criterion” governing the pricing process is defined for the algorithm, such as maximizing profit, maximizing revenues, etc. Advanced algorithms of this type are based on artificial intelligence, which enables them to make the decisions that implement the criterion defined for them at every decision junction, while constantly learning.
Companies are very well aware of the fact that these new pricing mechanisms will improve their results and increase their profits. For example, recent media reports note that Israir Airlines installed a dynamic pricing engine that boosted its profits significantly.
When it comes to pricing, algorithms offer significant advantages. AI-based algorithms are capable of performing complex analyses easily, efficiently, and quickly by processing an enormous volume of information. Their computational power far exceeds human capabilities when it comes to collecting, organizing, and analyzing information. This enables the reaching of decisions much faster and more accurately according to changing market conditions, all in real time. Another major advantage is that algorithms are capable of analyzing a company’s price history and, perhaps more importantly, the prices and price history of competitors, as well as their behavior under changing market conditions over time. This thus enables rapid and agile responses.
Pricing Engines and the Free Market
The adoption of such pricing engines may be a blessing, as they enable companies to better utilize their inventories and production capacity. However, at the same time, they may pose a risk of harm to competition, especially in markets with few competitors. One of the main principles underpinning competition, which leads to a reduction in prices, is a company’s uncertainty about its competitors’ future behavior.
This uncertainty does not allow a company to “rest on its laurels” and compels it be efficient, to improve the quality and variety of its products and services, and to reduce prices. Extensive use of algorithm-based pricing engines is likely to reduce these uncertainties, as they enable better and more accurate predictions of competitors’ future behavior, inter alia, by analyzing their past strategies. When programming algorithms to react by raising prices to the extent possible, it naturally raises concerns that the advantages of these pricing engines will undermine competition. Higher prices for consumers will ultimately reflect this consequence.
These concerns are likely to materialize rapidly in concentrated markets with few players, where competition failures already exist. In concentrated markets, companies can already predict the behavior of their competitors relatively easily, and thus behave in a less competitive manner and charge higher prices than would be charged under competitive conditions, even without explicit coordination between competitors. Now, algorithm-based pricing engines will make it even easier for players in concentrated markets to predict the behavior of their competitors. The ability to analyze competitors’ behavior and identify the characteristics of competitors’ pricing engines and their modes of operating will enable such companies to raise prices and maximize profits.
Unlawful Coordination between Pricing Algorithms
Technological progress cannot and should not be stopped. Therefore, the question arises of whether, and under what circumstances, competition law can intervene to prevent pricing algorithms from harming competition. And, if so, what tools are available for this purpose?
Within this context, it is important to focus on the distinction in competition law between “explicit coordination” (or “explicit collusion”), concerted practices and “parallelism” (also known as “tacit collusion”). xplicit coordination occurs when competitors reach an understanding or agreement about competitive issues between them (prices, quantities, customers, operating regions, etc.). In the vast majority of instances, authorities prohibit such explicit coordination, and certain jurisdictions may consider it a crime.
In the world of algorithm-based pricing engines, it is possible to raise an allegation of explicit coordination (or a less explicit coordination known as “concerted practice”), constituting a prohibited restrictive arrangement that harms competition and consumers. One such scenario is if competitors program their algorithms to “communicate” with each other and exchange information about the algorithms they are running, especially about the algorithms’ properties and the dominant criterion governing how they operate. (Theoretically, one algorithm can be programmed to raise prices in response to a competitor hiking prices, while another algorithm can be programmed to lower prices in response, in order to grab market share.) Another scenario is competitors agreeing to purchase the same algorithm with the same features. In such instances, competition authorities can employ the “classic” tools for dealing with cartels, including the filing of indictments (where applicable).
Algorithms Learn Market Conditions
Another possible challenge arises when the algorithm constantly “learns” market conditions and optimizes its operations to achieve its goals by independently learning to communicate directly with competitors’ algorithms, even though it was not programmed to do so. Could such “machine” communication constitute a prohibited restrictive arrangement? If so, can competition authorities hold a corporation and its officers liable for the coordination created by the AI-based algorithm or for their failure to prevent it at the outset? Furthermore, can the developer and programmer of the algorithm be held liable in such instance? These are extremely complex questions in uncharted territories that address the clash between the public’s interest in competition and the limits of protection of individual rights. This includs the extent to which authorities can hold a company or officer criminally liable for acts committed by an AI-based tool used by that company.
The situation becomes even more complex when it comes to “adjustments” or “parallelism”, i.e., when competitors are not coordinating competitive issues between them, but are merely adjusting their behavior, independently, according to their competitors’ behavior. Such adjustments (which the competition law does not prohibit) may harm competition and the public, especially in oligopolistic markets with few competitors. As noted above, the use of algorithm-based pricing mechanisms may increase the risk of such harm.
Competition Laws Need to Delegate Appropriate Powers
Unless the legislator amends competition laws to delegate appropriate powers, competition authorities will continue to have a hard time preventing competitive concerns in such instances. The Israel Competition Authority’s Director General possesses an authority that is almost unique in the world. Namely, the authority to declare competitors in oligopolistic markets (or in markets with few rules of competition) a “concentration group,” and to take measures against the members of the group that may prevent harm or concerns of significant harm to the public or to competition, or to take measures to significantly increase competition in a specific sector. The director general may order, inter alia, a member of the concentration group to discontinue a particular activity if that activity facilitates coordination among the other members of the group.
Theoretically, the director general may exercise this authority in instances of growing concerns of harm to competition resulting from use of algorithm-based pricing in markets with few competitors (or in markets characterized by competition failures). It is important to note that, although the delegation of this authority to the director general took place over a decade ago, there has been minimal exercise of this authority to date. To date, the competition authorities have declared only one concentration group, and they have issued orders to its members. It will be interesting to see if this technological development in pricing, which companies may use to legally raise prices using algorithms, will trigger higher use of this tool.
In any case, there is no doubt the use of algorithm-based pricing engines poses new and complex challenges for competition authorities. In due time, which may not be as far off as one might think, competition authorities will likely face the need to make difficult legal decisions in the scenarios described above or in similar ones.
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Barnea Jaffa Lande’s Competition and Antitrust Department is at your service to advise you on issues pertaining to artificial intelligence and competition, as well as other issues.
Adv. Gal Rozent heads the firm’s Competition and Antitrust Department.
Adv. Ran Karmi is an associate in the department.