Henry Hauser is counsel in Perkins Coie’s antitrust and litigation practice groups and has significant experience working on matters related to antitrust investigations and litigation under Sections 1 and 2 of the Sherman Act and Section 7 of the Clayton Act.
Shylah Alfonso Contributor
Shylah Alfonso is Firmwide Chair of Perkins Coie’s Antitrust & Unfair Competition Litigation Practice focusing on antitrust counseling and litigation, antitrust clearance for mergers and acquisitions, class action and complex commercial litigation, and intellectual property and fair, reasonable, and nondiscriminatory (FRAND) litigation.
Chris Williams Contributor
Chris Williams is a partner at Perkins Coie focusing on antitrust issues related to commercial transactions, including mergers and acquisitions (M&A); joint ventures and other strategic collaborations; licensing of intellectual property; and pricing, supply, and distribution agreements.
Antitrust is the engine of free enterprise: it shapes countless lines of commerce, from tech to toilets, beer to baseball and healthcare to hardware. Antitrust drives price, quality, variety, innovation and opportunity.
Today, artificial intelligence is rapidly changing how businesses sense, reason and adapt in the market. Across every industry, companies are leveraging machine learning to derive valuable insights without extensive employee involvement. But these groundbreaking capabilities are creating an upheaval in how companies engage with competitors and consumers.
Experienced competition and consumer protection lawyers can help companies capitalize on the opportunities AI presents while navigating the terra nova of regulatory and litigation risk. Although it is incorrect to approach AI as a black box, the complexity of AI systems can make reasoning opaque. This means linkages between AI outputs and rational business justifications risk being obscured or even lost entirely.
Yet regulators are unlikely to excuse consumer and competitive concerns merely because an organization cannot explain why certain actions were taken and others were not. Legal exposure exists under the Sherman Antitrust Act, Federal Trade Commission Act (FTC), Robinson-Patman Act, as well as state antitrust and consumer protection laws. By implementing policies and processes that preserve human control and accountability, organizations can minimize legal exposure and avoid unintended consequences.
A proactive and customized approach is critical. AI affects competition and consumers in countless ways, including when used for core business functions.
AI helps companies make pricing decisions by responding quickly to instantaneous changes in demand, inventory and input costs. By synthesizing and summarizing vast amounts of complex data, it can be a significant aid in building and adapting pricing policies. But the outcomes that AI-assisted pricing generates can also be seen as facilitating per se unlawful collusion, such as price-fixing or bid-rigging. According to FTC Chair Lina Khan, AI “can facilitate collusive behavior that unfairly inflates prices.”
These concerns may arise directly or indirectly from using AI to perform a diverse array of activities such as benchmarking, disaggregating information, signaling, exchanging information or analyzing pricing trends. Pricing algorithms, for example, may raise antitrust issues when competitors use them to enforce an advance agreement, algorithm vendors initiate or organize an agreement, companies apply algorithms to dramatically raise prices or even when competitors independently employ algorithms that subsequently engage in collusive conduct.
The U.S. Department of Justice’s Antitrust Division highlights that “the rise of data aggregation, machine learning, and pricing algorithms … can increase the competitive value of historical data” and warrants “revisiting how we think about the exchange of competitively-sensitive information.”