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Senators Consider Reforms to Antitrust Law to Address Algorithmic Price-Fixing and Self-Preferencing — AI: The Washington Report

Welcome to this week's issue of AI: The Washington Report, a joint undertaking of Mintz and its government affairs affiliate, ML Strategies.

This week, we discuss the Senate Judiciary Committee’s December 13, 2023 hearing entitled “The New Invisible Hand? The Impact of Algorithms on Competition and Consumer Rights.” Our key takeaways are:

  1. In this hearing, the committee members discussed how Congress should respond to novel forms of anticompetitive conduct made possible by advancements in computing and artificial intelligence. For Chair Amy Klobuchar (D-MN), “algorithms…have the potential to create or exacerbate competition problems.”
  2. The hearing centered on two such emerging modes of anticompetitive conduct: algorithmic price-fixing (the use of algorithms by firms to fix prices, either independently or in concert with other firms) and algorithmic self-preferencing (digital platforms’ deployment of algorithms to favor their offerings over those of competitors).
  3. Certain committee members, including Chair Klobuchar, suggested that Congress consider enacting legislation to “ensure that courts properly evaluate cases involving algorithmic price-fixing where an agreement is difficult to prove,” and to prohibit certain forms of algorithmic self-preferencing. 

Senate Judiciary Committee Discusses AI-Age Competition Concerns

The rapid development of AI systems has disrupted many existing regulatory paradigms, and antitrust law is no exception. For the Federal Trade Commission, in the AI age, “companies’ control over data may…create barriers to entry or expansion that prevent fair competition from fully flourishing.” Enforcement agencies are also beginning to consider how firms can deploy algorithms to anticompetitive ends, such as the preferencing of certain products or coordinated price-fixing.

Because of these concerns, the Department of Justice (“DOJ”) withdrew previously issued guidance on the collection and sharing of historical data that contained a safe harbor. The DOJ Antitrust Division recently filed an amicus brief in private treble damage litigation, contending the use of algorithms could constitute price-fixing or collusion that should be condemned as per se illegal.

To focus on some of these issues, the Senate Judiciary Committee’s Subcommittee on Competition Policy, Antitrust, and Consumer Rights held a hearing on December 13, 2023, entitled, “The New Invisible Hand? The Impact of Algorithms on Competition and Consumer Rights.”

Klobuchar Asserts that AI and Algorithms Are Driving Novel Forms of Anticompetitive Conduct

Chair Amy Klobuchar (D-MN) began the hearing by asserting that “algorithms…have the potential to create or exacerbate competition problems.” While advancements in computing and artificial intelligence can spur competition by opening up new modes of commerce, Chair Klobuchar suggests that these same advancements can harm competition by facilitating certain modes of anticompetitive conduct, including price-fixing and self-preferencing.

Price-fixing, traditionally considered by courts to be constituted by an explicit conspiracy between competitors, can now be conducted automatically and even independently by individual firms utilizing algorithmic pricing tools. Since such forms of algorithmic price-fixing often lack an explicit agreement, Chair Klobuchar expressed doubt that current antitrust laws could be used to effectively regulate this novel form of anticompetitive conduct. “Whether the conspiracy takes place in a server room, or a boardroom shouldn’t matter under the antitrust laws. But it isn’t clear whether our current antitrust laws are sufficient to stop that practice.”

In the absence of legislative reform, suggested Chair Klobuchar, price-fixing will become more pervasive and persistent. “We’ve always assumed that these price-fixing cartels are likely to form in highly concentrated industries…but algorithms actually can expand the number of industries in which price-fixing can occur.” By “minimizing the incentives to cheat” on the part of firms complicit in a price-fixing conspiracy, “algorithms can make price-fixing cartels more sustainable,” asserted the Chair.

Along with price-fixing, Chair Klobuchar stated that advancements in computing and AI are facilitating self-preferencing behavior. “Dominant platforms can and do use algorithms to preference their own products and services and bury those of their competitors.” To address these and other novel forms of anticompetitive conduct driven by technological development, Chair Klobuchar called for legislative form. “The United States needs laws that are up-to-date and as sophisticated as the monopolists we are trying to reign in.”

Klobuchar Suggests Reforms to Antitrust Law to Account for Advances in AI and Computing

To respond to these nascent modes of anticompetitive conduct, Chair Klobuchar suggested that Congress enact legislation to “ensure that courts properly evaluate cases involving algorithmic price-fixing where an agreement is difficult to prove.” Committee witness and former United States Assistant Attorney General William Baer, who led the Antitrust Division during part of the Obama Administration, responded to the Chair’s proposition by suggesting that Congress could respond to the issue of algorithmic price-fixing by legislating “a subtle tweak to section one of the Sherman Act…that directs the courts to take into account the anticompetitive impact of common-use pricing algorithms in the same market.”

Though a relatively novel form of anticompetitive conduct, algorithmic self-preferencing has been the subject of multiple bills introduced in Congress over the past few years. The Chair herself has introduced one such piece of legislation in the form of the 117th Congress’s American Innovation and Choice Online Act, an act that would prohibit certain kinds of self-preferencing behavior on the part of digital platforms.

Senators Suggest Data Privacy Reforms as a Means to Oppose and Minimize the Harm of Algorithmically Driven Anticompetitive Conduct

Both types of novel anticompetitive conduct discussed in the hearing, algorithmic price-fixing and self-preferencing, rely on the operation of powerful algorithms, which in turn rely on the collection and processing of massive collections of data. The assembled senators, therefore, identified data collection as an issue of primary importance regarding computationally driven anticompetitive conduct.

In the course of the hearing, senators identified mass data collection as both a precondition for the creation of algorithms that enable these novel forms of anticompetitive conduct, and a negative impact of the continued operation of these algorithms. “The more companies rely on algorithms,” asserted Chair Klobuchar, “the more they will be incentivized to track consumers to collect data, raising privacy concerns.”

Given the seemingly central role of data collection in the operation of algorithmic self-preferencing and price-fixing schemes, certain committee members advocated for the passage of data privacy reforms to address these modes of computationally driven anticompetitive conduct. Senator Peter Welsh (D-VT) raised the possibility of mandating that “a user should have to give their express…consent before an AI system harvests their data” as a means to promote competition. Senator Mazie Hirono (D-HI) even suggested the establishment of a “new dedicated digital platform regulator” as an alternative to “bulking up our current antitrust enforcers.”

Greater Enforcement of Antitrust Law as an Alternative to Legislative Reform

Not all members of the committee so readily endorsed the enactment of legislative reform to respond to algorithmic price-fixing and self-preferencing.

Ranking Member Mike Lee (R-UT) instead emphasized the thorough enforcement of existing antitrust law to address these novel forms of anticompetitive conduct. “Google and Facebook are a digital advertising duopoly,” asserted Ranking Member Lee. “Breaking up the AdTech stack is an important first step towards reigning in the power of Big Tech platforms and making sure that they don’t use algorithms in an anticompetitive, predatory, harmful, manner.”

However, even Ranking Member Lee expressed doubt that existing standards could respond to these novel challenges in a timely fashion. “Enforcement and litigation under existing authorities may well be capable of addressing the problem” of firms abusing algorithms to anticompetitive ends, suggested Ranking Member Lee. However, Lee wondered aloud whether it might take so long for existing authorities to successfully regulate these novel forms of anticompetitive conduct that relying on existing authorities may not be “ideally suitable to deliver the win for competition and therefore the consumer that you need.”

We will continue to monitor, analyze, and issue reports on these developments.

With research assistance from Margaret Kelley.


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Bruce D. Sokler

Member / Co-chair, Antitrust Practice

Bruce D. Sokler is a Mintz antitrust attorney. His antitrust experience includes litigation, class actions, government merger reviews and investigations, and cartel-related issues. Bruce focuses on the health care, communications, and retail industries, from start-ups to Fortune 100 companies.

Alexander Hecht

ML Strategies - Executive Vice President & Director of Operations

Alexander Hecht is Executive Vice President & Director of Operations of ML Strategies, Washington, DC. He's an attorney with over a decade of senior-level experience in Congress and trade associations. Alex helps clients with regulatory and legislative issues, including health care and technology.

Christian Tamotsu Fjeld

Senior Vice President

Christian Tamotsu Fjeld is a Vice President of ML Strategies in the firm’s Washington, DC office. He assists a variety of clients in their interactions with the federal government.

Raj Gambhir

Raj Gambhir is a Project Analyst in the firm’s Washington DC office.