Pricing Algorithms
Price Tags and Personal and Competitor Data: States Step Up Algorithmic Pricing Regulation
As price-setting by computer algorithm becomes increasingly prevalent, states are stepping in to address transparency and fairness concerns that federal legislation has yet to comprehensively tackle. Lawmakers argue that clear disclosure and limits on algorithmic practices are essential to protect consumers from opaque pricing methods that may leverage their personal data or result from anti-competitive collaboration among businesses. The growing patchwork of state-level initiatives signals a broader trend toward local oversight of algorithmic decision-making in commerce, but the landscape is rapidly changing as lawmakers attempt to catch up to rapidly changing technology.
As they are often at the forefront of these issues, recent legislative and regulatory developments in California and New York are leading the way on regulating the growing technology. In the meantime, federal courts have issued divided decisions dealing with algorithmic pricing.
New York: Algorithmic Pricing Disclosure Act Survives Legal Challenge
In May 2025, New York passed the Algorithmic Pricing Disclosure Act, requiring businesses to inform customers when prices are set using personalized algorithms. The Act broadly applies to entities domiciled or doing business in New York. The Act requires businesses to disclose when a price is set using an algorithm that incorporates personal consumer data by requiring the following disclosure: “THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA.” The New York Act is enforced solely by the New York Attorney General, who must first issue a cease-and-desist notice before pursuing penalties of up to $1,000 per violation.
The passage of the New York law marked a significant milestone, as it recently withstood a legal challenge brought by industry groups who argued that the mandated disclosure infringed on commercial free speech and imposed undue burdens on businesses.[1] On October 8, 2025, the court granted New York State’s motion to dismiss, finding the disclosure was factual and uncontroversial and that it served a valid consumer protection interest.
California: Restrains use of Competitor Data to Influence Price
On October 6, 2025, California signed AB 325 into law. AB 325 prohibits agreements to use or distribute a “common pricing algorithm,” defined as any software or other technology that two or more people use which ingests competitor data to recommend, align, stabilize, set, or otherwise influence a price or commercial term (including terms related to both upstream vendors and downstream customers), and lowers the pleading standard under the Cartwright Act (California’s antitrust statute, Cal Bus. & Prof. Code § 16720) for certain civil claims. The law also prohibits coercing another person to set or adopt a recommended price or commercial term generated by such an algorithm for the same or similar products or services in California.
Other Efforts to Regulate Algorithmic Pricing
In 2025 alone, more than 50 bills have been introduced to regulate algorithmic pricing across 24 state legislatures, including the following:
- Illinois introduced several bills that, if enacted, would regulate or ban dynamic pricing in selected situations, including ticket sales (HB 3838) or the use of consumer data in pricing (SB2255).
- Texas introduced SB 2567, which would require retailers to disclose algorithmic pricing at the point of sale.
- Massachusetts introduced House Bill 99 which, if enacted, would ban dynamic pricing based on customers’ biometric data.
- Colorado’s legislature passed, but the governor vetoed, HB25-1004, legislation that would have prohibited the sale or distribution of an algorithmic device sold or distributed with the intent to be used by two or more landlords in the same market to set or recommend the amount of rent, level of occupancy, or other commercial terms.
- New Jersey introduced SB 3657, which seeks to make it unlawful for landlords or property managers to use algorithmic systems to influence rental prices or housing supply in New Jersey.
- Pennsylvania introduced HB 1779, which seeks to require disclosure of algorithmic pricing and prohibits dynamic pricing based on protected class data (e.g., race, gender, religion).
Last week, U.S. Senators Ron Wyden and Peter Welch introduced The End Rent Fixing Act of 2025. The Act is targeted at algorithms that use competitors’ data to set rental rates. The Act would make it unlawful for rental property owners to contract for the services of a company that coordinates rental housing prices and supply information and would designate such arrangements as a per se violation of the U.S. antitrust laws. It would also prohibit the practice of coordinating price, supply, and other rental housing information among two or more rental property owners. The Act would also allow individual plaintiffs to invalidate any pre-dispute arbitration agreement or pre-dispute joint action waiver that would prevent the plaintiff from bringing suit.
Algorithms Using Competitors’ Data to Set Prices
U.S. antitrust law hasn’t fully caught up with how algorithmic price setting, and the legal landscape, is changing fast. Some experts think there could be liability in certain situations. For example, the Department of Justice has argued that if competitors use the same pricing algorithm—and that algorithm relies on competitors sharing their data to set prices—it could violate the Sherman Antitrust Act.
In September 2025, the Ninth Circuit issued the first federal appellate decision on algorithmic pricing in Gibson v. Cendyn Group, ruling that competing Las Vegas hotels did not violate Section 1 of the Sherman Act by independently using the same third-party pricing software, where there was no underlying agreement among competitors and the software did not share confidential information among licensees.
In contrast, in December 2023, an Illinois federal court denied motions to dismiss claims in multi-district class action litigation alleging software vendors and rental property owners and managers conspired by sharing property rental pricing and supply data to fix prices for multi-family house rentals across the country.[2] Last week, the court granted preliminary approval of settlements with 27 defendants for $141.8 million. The litigation continues with the larger defendants whose conduct, the plaintiffs contend, comprised the larger volume of the alleged illegal commerce at issue in the case.
In June 2025, an Illinois federal court denied a motion to dismiss allegations that health insurers unlawfully conspired to underpay out-of-network providers by outsourcing rate-setting to analytics firm MultiPlan. The court applied the per se standard, finding plaintiffs “plausibly alleged a horizontal hub-and-spokes price-fixing agreement.”
Conclusion
The legislative developments and growing litigation over the legality of dynamic pricing tools reflect growing concern among policymakers about the fairness and transparency of algorithmic pricing models. As states continue to debate and refine proposed laws, businesses that rely on dynamic pricing must closely monitor these changes and proactively assess their compliance obligations. Staying informed about both state and federal actions will be essential to avoid potential legal pitfalls and ensure responsible use of pricing algorithms.
Our team is available to assist with legal reviews, compliance strategies, and AI governance planning. If you have questions about how statutes, regulations, or court rulings impact you or your business, contact your Miller Canfield attorney or one of the authors of this alert.
[1] National Retail Federation v. James, 1:25-cv-05500-JSR
[2] In Re: RealPage, Inc., Rental Software Antitrust Litigation, Case No. 3:23-md-3071, MDL No. 3071.
