Key Takeaways:
- Earlier this year, New York enacted its Algorithmic Pricing Disclosure Act, a first-of-its-kind statute that requires certain businesses to include a written disclosure if a price is set using an algorithm based on personal data: “THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA.”
- A federal judge recently dismissed a challenge to the Act, rejecting arguments that the law violated free speech rights and finding that the statute is rationally related to the state’s consumer protection interests.
- The decision is notable and it will likely add to the growing scrutiny of algorithmic or so-called “surveillance” pricing at both the state and federal levels.
- Businesses using or considering algorithmic pricing systems should reassess legal risk, update compliance programs and develop procompetitive justifications for algorithmic pricing strategies.
On October 8, 2025, the U.S. District Court for the Southern District of New York dismissed a lawsuit challenging New York’s newly enacted Algorithmic Pricing Disclosure Act, New York Gen. Bus. Law § 349-a. The Act requires affected retailers[1] to include a written disclosure alongside every price that is derived from or set by an algorithm using personal data. The disclosure must state: “THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA.”
In his opinion, Judge Jed Rakoff rejected claims from retailers that the statute was compelled speech and therefore violated the First Amendment. The court held that the disclosure is factual, uncontroversial and reasonably related to the state’s interest in consumer transparency. The court applied the test from Zauderer v. Office of Disciplinary Counsel, 471 U.S. 626, which permits compelled commercial disclosures if they are not unduly burdensome and are reasonably related to the state’s interest in preventing consumer deception. In so ruling, the court found that the disclosure is factually accurate and not misleading, and it was irrelevant that a regulated entity would prefer not to make the disclosure or to make a different statement on the same topic. Therefore, the court explained, nothing in the Act required retailers to “take sides” in any public debate.
Enforcement of the Act was stayed during the litigation and, absent the entry of a stay pending appeal, the Act will take effect immediately. The ruling affirming the constitutionality of the Act will likely add to the regulatory momentum around algorithmic pricing and use of customer data in dynamic pricing. In 2025 alone, at least 50 bills that are intended to regulate algorithmic pricing have been introduced across 24 state legislatures, with a focus on retailers that set prices based on consumer-specific data (e.g., browsing history, location, demographics, etc.). Some of the more notable statutes include:
- California’s legislature enacted AB 325, which amends California’s antitrust law to prohibit the use or distribution of an algorithm that is trained with nonpublic competitor data, and to further prohibit the use or distribution of any algorithm with the intent that two or more competitors would use the algorithm to set prices.
- Illinois has 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 setting price (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 if the device is sold or distributed with the intent that it will 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 term.
While legislative and disclosure risk is growing, retailers must also remain attuned to antitrust vulnerability from algorithmic pricing strategies. This risk is particularly high where there is tacit or explicit coordination between companies to use algorithms that rely on nonpublic data to generate prices or other information that may affect price, such as output. In addition to risks to retailers, developers of software that collect and process nonpublic pricing data may also face antitrust claims where the use of the software is part of a price fixing scheme.
Practical Considerations
For retailers that use a dynamic pricing algorithm that relies, at least in part, on consumer data to set prices, immediate action is required in order to comply with New York law. Retailers should:
- Build state-by-state disclosure capabilities. Web-based or app-based interfaces should include a user interface or user experience element that triggers the required disclosure or notice in states that require notice. Although notice is only currently required in New York, other states are likely to adopt similar laws.
- Assess and inventory your algorithm use and data inputs. Determine what algorithms are used to set price and flag whether those algorithms use consumer-specific data, such as location, browsing history, shopping history or biometric inputs. An inventory of this information will be helpful to analyze and build compliance strategies for other laws that may be adopted.
- Avoid pooled use of nonpublic data to set prices. The coordinated use of algorithms, or the use of algorithms trained on nonpublic data, is explicitly unlawful under California’s antitrust law and an increasing number of plaintiffs have challenged the practice under federal law with mixed results. Identify vendors that may be utilizing nonpublic competitively sensitive data in connection with their work and consider contractual or technical safeguards to avoid inadvertent exposure to competitors’ nonpublic data.
- Monitor state legislation. Legislatures are increasingly active in seeking to regulate dynamic and algorithmic pricing. Retailers should actively monitor pending legislation and prepare for additional restrictions or disclosure requirements on these practices.
Retailers that operate in New York – or that use pricing algorithms that may impact New York customers – should take immediate steps to assess their compliance with the new law. As regulatory scrutiny around algorithmic and dynamic pricing intensifies, a compliance plan is critical to ensure transparency to customers, mitigate legal risk, and maintain consumer trust. Benesch’s Retail & E-Commerce Group has closely monitored the developments in this area and can assist you in navigating this new compliance environment.
[1] Some retailers are exempted from the Act: certain local for-hire services that rely on location data for trip pricing (New York Gen. Bus. Law § 349-a(1)(d)); consumer insurance products; certain consumer financial products; and certain goods sold under a subscription agreement. New York Gen. Bus. Law § 349-a(3).