When Your Data Sets Your Price: New York Takes on Algorithmic Pricing

It has long been the case that certain sales and service providers increase or decrease consumer prices based on perceived data.

A luxury item purveyor or home improvement contractor, for example, may look at a potential customer’s overall appearance, personal items such as a watch or purse, or their address or profession, to determine a quoted price. This practice is not just improper and deceitful—it is exploitative, and these perceived datapoints may not reflect the consumers’ actual purchasing power. Yet this type of variable pricing wasn’t an issue typically associated with general retail, until artificial intelligence (AI) arrived.

Dynamic Pricing and Surveillance Pricing

As reported by the New York Post, Instacart—a technology platform offering grocery ordering, delivery, and pickup—has been using a dynamic pricing algorithm that charges customers different prices for the very same items from the same grocery stores. Major grocery retailers, meanwhile, are now implementing electronic price tags so staff can adjust shelf prices in-store within minutes. While dynamic pricing itself is not new, AI has made it significantly easier to change prices automatically and instantly.

The same article notes that ride-share platforms use dynamic pricing, often referred to as “surge pricing,” to increase prices when demand is high. Prices are often higher during periods of high demand, including on holidays, at rush hour, at the end of a concert or sporting event (they may even depend on the weather). While no one likes paying more for an Uber on a rainy night, surge pricing reacts to a shift in market demand, not based on the individual user.  These platforms have argued that they are engaging in supply and demand, a fundamental and time-honored principle of capitalism. Compare that to the more controversial “surveillance pricing,” whereby companies track users’ extensive personal data with AI. Surveillance pricing algorithms often rely on a user’s precise geographic location, browsing history, and purchase history to determine the price that the user might be willing to pay.

New York State Algorithmic Pricing Disclosure Act

New York has become the first state to enact an Algorithmic Pricing Disclosure Act. Article 22-A, “Consumer Protection From Deceptive Acts and Practices,” Section 349-A on “Pricing,” imposes requirements on entities pricing a good or service using personalized algorithmic pricing, based on consumers’ personal data. Such entities must include with the statement, display, image, offer, or announcement a clear and conspicuous disclosure that states, “This price was set by an algorithm using your personal data.”

The law was signed by Governor Kathy Hochul on May 9, 2025, effective November 10, 2025. It appears broad in scope and applies to retailers operating in the state, with exceptions for insurers, financial institutions and their affiliates, and certain subscription contracts for goods or services.

Violations will be based on consumer complaints and reports filed with the state attorney general, who will provide a cease-and-desist notification to the alleged wrongdoer with an opportunity to cure the violation within a designated time. Failure to abate the violation within the timeframe for cure will allow civil penalties of $1000 per violation and injunctive relief.

New York Attorney General Letitia James issued a consumer alert warning on November 5, 2025, encouraging New Yorkers to file a complaint with her office should they encounter algorithmic pricing; and warning businesses of the $1,000 penalty per violation.

Federal Activity

In January 2025, the Federal Trade Commission (FTC) released its initial findings from its surveillance pricing market study. The findings revealed that a wide range of personal data, including precise location, demographic data, search history, and even mouse movements, is frequently used to set individualized consumer prices for the same goods and services. The study focused on third-party intermediary firms, which are the middlemen that retailers hire to algorithmically set and adjust individual prices. The FTC found that intermediaries work with at least 250 clients, including grocery store and apparel retailers.

On November 24, 2025, the United States Department of Justice (DOJ) Antitrust Division filed a proposed settlement to resolve the government’s claims against RealPage, Inc., a commercial revenue management software provider for the rental housing industry. RealPage faced allegations of “algorithmic collusion,” specifically creating algorithms that allowed landlords to illegally collude and increase rent prices for tenants. This case marked the first time DOJ antitrust enforcers went after algorithmic collusion.

State-Level Activity

Meanwhile, other states are considering bans on algorithmic pricing. In 2025 alone, 24 different state legislatures introduced over 50 bills, all to regulate algorithmic pricing. As Forbes has written, we may see a national patchwork of laws, if not a federal standard. Stakeholders in this area should be aware of the emerging landscape.

Illinois introduced several bills that would regulate dynamic pricing in ticket sales (HB 3838) or the use of consumer data in pricing (SB 2255). A Texas bill (SB 2567) would require retailers to disclose algorithmic pricing at the point of sale. Pennsylvania introduced HB 1779, which would require the disclosure of algorithmic pricing, as well as prohibit dynamic pricing based on protected class data. Massachusetts introduced HB 99, which would ban dynamic pricing based on biometric data.

California’s proposed SB 384, the “Preventing Algorithmic Price Fixing Act,” for example, provides that “[a] person should not sell, license, provide, or use a price-setting algorithm with the intent or reasonable expectation that it be used by two or more competitors in the same market if the person knows or should know that the algorithm processes nonpublic input data to set either…1) [a] price or supply level of a good or service, or 2) [a] rent or occupancy level or rental property.”  

As discussed above, software used to set and coordinate rental prices among competitors using personal data sparked a proposed DOJ antitrust settlement in November. New Jersey introduced SB 3657, which seeks to prohibit landlords and property managers from using algorithms to influence price and supply of residential rental properties in New Jersey.

While New York’s Algorithmic Disclosure Pricing Act applies to goods and services, Governor Hochul signed another bill in October 2025, SB 7882, which provides that the use of an algorithm or algorithmic device to adjust rental price levels in the residential rental market is unlawful collusion.

EBG will continue to monitor developments in this area. If you have any questions on the application of the law to your organization, please reach out to the authors or to an EBG attorney with whom you work.

Epstein Becker Green Staff Attorney Ann W. Parks contributed to the preparation of this post.