For an alternate viewpoint, see “Point: Voters Fear Current Methods of Regulating AI are Insufficient.

With new technology comes new possibilities. A side effect is where these possibilities fit into existing law. Dynamic, or algorithmic, pricing is a strategy where artificial intelligence uses data collected about market conditions to determine pricing in real time.

Algorithmic pricing has been a concern for antitrust regulators for years, even before the AI boom. While a re-examination of laws due to changing circumstances is a normal part of progress, there is currently insufficient evidence to suggest that dynamic pricing is leading to conspiracy. The potential for issues is not enough to show that they are actually occurring, and lawmakers should not create a solution for a problem that doesn’t exist.

Earlier this year, a class-action lawsuit was filed against several Las Vegas companies, including MGM Resorts and Caesars Entertainment, arguing that the companies used the same dynamic pricing software. The lawsuit claims this led to elevated hotel prices. The U.S. District Court in Nevada dismissed the case, saying the plaintiffs did not provide enough evidence of collusion between the companies but did give the claimants 30 days to submit a revised complaint addressing the issue.

While the lawsuit was recent, concern about dynamic pricing using algorithms is nothing new. In the field of antitrust, some have raised the possibility that these programs could lead to collusion.

The Federal Trade Commission released a public statement in 2017 discussing this possibility. The statement explored how algorithms could facilitate collusion in different ways. Either purposefully by making it easier for companies colluding to respond to lower prices by a company trying to undercut them or autonomously as the AI behind the software learns and decides on an anti-competitive strategy.

The complaint in the case against the hotels argues that using the same software means that the hotels don’t have to price independently. The complaint also claims that academic research supports the idea that dynamic pricing algorithms lead to anticompetitive behavior and higher consumer prices. Some have argued that regulators should explore implementing new regulations in response to the possibility of anticompetitive effects from algorithmic pricing.

Despite claims that algorithmic pricing could lead to anticompetitive behavior and collusion, the evidence is inconclusive. While the potential for intentional and tacit collusion exists in theoretical models, there are numerous obstacles to implementing this in a real-world setting. A primary barrier is that algorithms are not advanced enough to achieve optimal pricing in real-world applications, as many algorithms do not consider essential factors such as product differentiation and the effect of advertising on consumer choice.

The first major real-world test of dynamic pricing in antitrust law also found that evidence for collusion was lacking. In dismissing the court case, the court found insufficient evidence to suggest a conspiracy between the defendants. Instead, the plaintiffs inferred collusion, which is not enough to prove it occurred. Lawsuits like this and calls for regulation on what may happen should be tempered by this lack of evidence.

While automation may make collusion easier in the future, that is not sufficient to justify expanding regulation. If there comes a time when dynamic algorithmic pricing is used to create market conditions detrimental to consumers, then the topic can be revisited. As it now stands, there is not sufficient evidence to warrant regulatory intervention. Intervening directly would be creating a solution without a problem.