The US Regulatory Landscape of “Surveillance Pricing”

The US Regulatory Landscape of “Surveillance Pricing”

Overview

Ever wonder if you’re paying more for the exact same item as another consumer? You may be. Pricing that is set for an individual consumer using that consumer’s personal data — often called “surveillance pricing” or “personalized algorithmic pricing” — has quickly moved from an interesting academic discussion and policy debate to an active compliance problem. New York’s disclosure law took effect in November 2025. Maryland, Connecticut and New Jersey enacted grocery- or retail-focused bans in 2026. Dozens of other surveillance pricing bills are pending in states across the country. At the federal level, while Congress has yet to act, the FTC is using existing authority to investigate pricing practices that rely on personal data.

Key Distinction: Dynamic Pricing vs. Surveillance Pricing

Most of the new laws and regulations distinguish between “dynamic pricing” and “surveillance pricing.” Dynamic pricing adjusts prices based on aggregate market signals, such as supply and demand, time of day or season, and is generally not the target of current regulatory activity. Surveillance pricing uses data about a specific person, such as browsing history, location, demographics, income, purchase history, or device type, to set an individualized price based on who the consumer is and what that consumer is believed to be willing to pay.

Enacted State Laws

New York — Algorithmic Pricing Disclosure Act (N.Y. Gen. Bus. Law § 349-a)

An entity that sets a price with personalized algorithmic pricing using a New York consumer’s personal data must display, clearly and conspicuously and with the price, the statement “THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA.” Exemptions cover insurance-regulated entities, GLBA-regulated financial institutions, certain ride-hail fare calculations and certain subscription discounts. Civil penalties are up to $1,000 per violation, enforced by the Attorney General, with no private right of action. Signed May 2025; in effect since November 10, 2025. Survived a First Amendment challenge in S.D.N.Y; appeal pending in the Second Circuit.

Maryland — Protection from Predatory Pricing Act (H.B. 895)

Applies to grocery stores of at least 15,000 square feet and to third-party food delivery services. Bars using personal data to set a shopper-specific price for most groceries. Permits loyalty programs, subscription pricing and differences based on supply, location or operating costs. Businesses get a 45-day cure period. Effective October 1, 2026.

Connecticut — Public Act 26-130 (H.B. 5563)

Broadly bars retailers and third-party delivery services from surveillance pricing, with exceptions for discounts and price differences unrelated to personal data. Requires the label “THIS PRICE WAS INCREASED BY A PRICE SETTING DEVICE USING YOUR PERSONAL DATA.” Signed June 4, 2026; effective October 1, 2026.

New Jersey — Fair Price Protection Act (A4085)

Makes it an unlawful practice under the Consumer Fraud Act for a retail food store or third-party grocery delivery platform to vary grocery prices based on personal data, in store or online. That data includes browsing history, real-time location, inferred family size or income, biometric and genetic data, and protected-class status. Loyalty programs, bona fide group discounts and cost-based differences are permitted. Location data may be used for fulfillment and to assess local supply and demand. Installation of new electronic shelf labels is paused for one year. Penalties are up to $50,000 per violation or actual damages, plus restitution and treble damages, and there is a private right of action. Signed July 23, 2026; most provisions take effect August 1, 2027.

Examples of Pending State Legislation

New York: One Fair Price Act (S.8623B/A.9349B)

In June 2026, the state legislature passed the One Fair Price Act; it awaits Governor Hochul’s signature. If enacted, it would amend § 349-a to ban surveillance pricing outright and to prohibit collecting, using, retaining or sharing personal data to facilitate it. Personal data for this purpose includes purchase and browsing history, real-time location, income and inferred household size. Dynamic pricing algorithms that do not use personal data would remain permissible, but those that change prices more than once a day would face a modified disclosure requirement. Penalties would be up to $5,000 for a first violation and up to $20,000 for each later violation, or the profits from the violation if greater. A private right of action was removed during negotiations.

New Jersey: Broader Bills Beyond Groceries

The Fair Price Protection Act covers only the sale of groceries. Several New Jersey bills that would apply to other categories of goods and services remain pending, including:

• S3612. This bill would make it an unlawful practice under the Consumer Fraud Act to use personalized algorithmic pricing, surveillance pricing or any strategy that sets or varies the price of merchandise or services based, in whole or in part, on a consumer’s personal data, including biometric, genetic and protected-class data. It is not limited to any sector. Penalties would follow the Consumer Fraud Act: up to $10,000 for a first offense and $20,000 for later offenses, plus cease-and-desist orders and treble damages. The Division of Consumer Affairs would have rulemaking authority.

• S4314. This bill would ban personalized algorithmic pricing and surveillance-based pricing by four kinds of business: online retailers with at least $10 million in annual gross New Jersey sales, airlines, ticket brokers and transportation network companies.

• S3732. This bill would bar retail grocers and third-party grocery delivery platforms from using dynamic pricing, surveillance pricing or personalized algorithmic pricing. This bill is unusual because it would also apply to dynamic pricing.

California

California’s AB 446 would bar grocery establishments from offering customized price increases based on personally identifiable information collected through electronic surveillance technology. It has exceptions for cost-based differences, loyalty programs the consumer enrolls in, and discounts the consumer knowingly trades data for.

Federal Landscape

• Federal Trade Commission: In July 2024, the FTC issued Section 6(b) orders to gather information from eight companies about surveillance pricing. A January 2025 FTC report found the practice widespread. Under Chairman Ferguson, the agency has generally moved away from broad rulemaking and toward targeted enforcement. However, its April 14, 2026 Advance Notice of Proposed Rulemaking on online food and grocery delivery fees expressly asks whether platforms tell consumers when they are charged personalized prices. In May 2026, sixteen state attorneys general urged the FTC to issue a separate rule on surveillance pricing.

• Congress: No federal surveillance pricing statute has been enacted. Bills include the One Fair Price Act (S. 3387) and the Stop Price Gouging in Grocery Stores Act (H.R. 4966). The House Oversight Committee opened an investigation into surveillance pricing in March 2026, and Energy and Commerce Committee Democrats opened a parallel inquiry in May 2026. On August 4, 2026, a Senate Judiciary subcommittee held a hearing on AI surveillance pricing.

State Attorney General Enforcement

State attorneys general are not waiting for new laws and regulations; they are using their existing authority under state consumer protection laws. In January 2026 the New York Attorney General wrote to Instacart about reported price differences among shoppers and about its compliance with § 349-a. The California Attorney General similarly began a surveillance pricing sweep of the grocery, retail, hotel and travel sectors, on the theory that using consumer data in ways that go beyond consumers’ reasonable expectations may violate the CCPA.

Conclusion

Collecting and analyzing personal data allows companies to find their pricing “sweet spot” – the highest amount a given consumer is willing to pay for an item. That is a powerful transactional advantage that companies are unlikely to relinquish voluntarily. Time will tell whether the practice can be effectively curtailed through a robust regime of regulation and a likely wave of class-action litigation.

Article provided by INPLP member: Jason Kravitz (Nixon Peabody, United States)