Federal Algorithmic and Surveillance Pricing Bills

Federal Bills Targeting Algorithmic and Surveillance Pricing

Three federal bills discussed around surveillance pricing address different conduct. S. 232 focuses on pricing algorithms that use nonpublic competitor data. H.R. 4640 would prohibit defined surveillance-based price and wage setting while providing specified routes and conditions. S. 3387 would prohibit different consumer prices for the same or substantially similar offer when the difference is informed by surveillance data, subject to safe harbors.

As of July 27, 2026, all three remain introduced. None has passed its chamber of origin or become law.

This is a dated plain-language guide, not legal advice. The official text and action history control.

Current status

A bill marked introduced has entered the legislative process. It has not cleared committee, received approval from both chambers, or reached the President. Public attention, a hearing, a lawsuit, or a growing cosponsor list does not establish a likelihood of enactment.

S. 232 addresses competitor data and coordination risk

The introduced text of S. 232 defines a pricing algorithm broadly as a computational process that processes data to recommend or set a price or commercial term. Its central restriction would make it unlawful to use or distribute a pricing algorithm that uses, incorporates, or was trained with nonpublic competitor data.

Nonpublic competitor data includes information derived from or provided by a competitor in the same or a related market. The definition covers nonpublic information about prices, commercial terms, and related products or services even when the data is anonymized. It excludes generalized reporting that does not reveal underlying competitor data.

The bill would also create a competition-law audit. On written request from the Attorney General or FTC, a user or distributor would have to report information about the algorithm, including the responsible developer, whether the system acts autonomously, human review, rules, data, data sources, collection frequency, and whether it differentiates among similar customers or workers. A senior corporate officer would certify the report under penalty of perjury.

For a person with at least $5 million in annual revenue, S. 232 would require disclosure before a purchase or work arrangement when a pricing algorithm recommends or sets the relevant price or commercial term. Additional disclosure would apply when the system differentiates among similar transactions or comes from a third party.

flowchart LR
    A["Nonpublic competitor data"] --> B["Pricing algorithm"]
    B --> C["Recommendation or commercial term"]
    C --> D["Coordination and antitrust risk"]
    E["Personal or inferred consumer data"] --> F["Individualized offer"]
    F --> G["Surveillance-pricing risk"]

S. 232 is often placed inside the surveillance-pricing debate because it also creates transparency around pricing algorithms. Its core prohibition is not a general ban on personalization. It targets competitor data and potential coordination.

H.R. 4640 addresses customized prices and wages

The introduced text of H.R. 4640 would prohibit surveillance-based price setting. It defines the practice as using an automated decision system to offer or inform a customized price for a person or group based in whole or part on surveillance data.

The definition of surveillance data includes information obtained through observation, inference, or surveillance that relates to personal information, genetics, behavior, biometrics, or a group, band, class, or tier. It includes gathered, purchased, or otherwise acquired information.

The proposal identifies three routes that would not count as prohibited surveillance-based pricing when all additional conditions are met. These involve reasonable cost differences, disclosed broad-group discounts, and discounts through an affirmatively joined loyalty, membership, or rewards program.

The additional conditions include clear eligibility disclosure, uniform treatment of people who meet the criteria, and limits on using surveillance data for another purpose. The text also calls for public procedures addressing data accuracy, consumer correction or challenge, and disclosure of what data the automated system considers and how it uses that data.

A separate section would prohibit surveillance-based wage setting. It provides a route when the system uses only the city or state where the person works and the cost of living there. It also calls for disclosure, accuracy, and challenge procedures.

The bill provides for FTC enforcement, state actions, and private actions with specified remedies. Those provisions describe a proposal. They do not create present-day duties unless enacted.

S. 3387 focuses on different prices informed by surveillance data

The introduced text of S. 3387 would make it unlawful to offer or charge different consumers different prices for the same or substantially similar product or service when the difference is informed in whole or part by surveillance data.

Its surveillance-data definition covers information related to a person's personal information, behavior, or biometrics and includes gathered, purchased, or otherwise acquired data.

The proposal provides safe-harbor treatment for reasonable cost differences, broadly defined group discounts, and loyalty-program discounts when its conditions are met. Cost differences and eligibility conditions would need advance disclosure. Qualifying discounts would need uniform availability to people who meet the stated criteria. Surveillance data used to administer a discount could not also be used for profiling, targeted advertising, or individualized price setting.

S. 3387 would not apply its general prohibition to the business of insurance or credit products. It would separately reach air carriers, foreign air carriers, and ticket agents through a proposed amendment to federal aviation law.

The bill includes FTC and state enforcement plus a private right of action. In a private action, specified contemporaneous price differences could create a presumption of violation. A defendant could rebut it by showing that surveillance data did not inform the difference or that a safe harbor fully explained it.

That is more precise than saying the company always bears the burden to justify any dynamic price. The presumption arises within a defined cause of action and has defined rebuttal routes.

The proposals differ at the input

The comparison prevents a common mistake. Algorithmic collusion and surveillance pricing can coexist, but one does not prove the other. A system can coordinate sellers without identifying individual customers. A seller can personalize offers without using competitor data.

Where RealPage fits

The Justice Department's RealPage case record identifies alleged collusion and agreements not to compete. DOJ alleged that RealPage revenue-management software relied on landlords' nonpublic, competitively sensitive information and included features that limited decreases or aligned pricing.

DOJ's November 2025 proposed settlement announcement describes proposed restrictions on runtime competitor data, recent active-lease training data, geographic effects, market surveys, specified software features, and related conduct.

That fact pattern is closely connected to S. 232's competitor-data focus. It is not evidence that RealPage estimated a renter's willingness to pay from that renter's personal data. [[RealPage Company Profile]] keeps the company record, allegations, and procedural status separate.

What these bills do not establish

The existence of a proposal does not prove that every covered practice is lawful today, nor does it prove that existing law never applies. Antitrust, consumer-protection, privacy, civil-rights, sector, contract, and state rules may apply depending on the conduct and evidence.

The Robinson-Patman Act is also not a substitute summary. The FTC's guide explains that the statute has specific requirements involving commodities, competing purchasers, timing, interstate commerce, and possible injury to competition, along with defenses. It is not a general federal ban on individualized consumer prices.

How to monitor the bills

The durable monitoring record is simple. Check the Congress.gov overview for status and latest action. Open the latest official text rather than assuming the introduced version still controls. Review committee, amendment, companion-bill, and action pages. If a bill advances, compare the new text section by section because a familiar title can conceal a changed definition, exception, remedy, or effective date.

This guide should be refreshed after any listed action and at the end of the 119th Congress. Until then, the accurate description is three introduced proposals with overlapping motivation and distinct operative language.

For the underlying terminology, read [[What Is Surveillance Pricing]]. For a product review independent of enactment, use [[When Is Variable Pricing Fair]]. Readers following the grocery thread can continue with E099's [[Algorithmic Grocery Pricing - What Shoppers Can and Cannot See|algorithmic grocery pricing evidence guide]].

This guide was developed from Congress.gov, DOJ, FTC, and the preserved E096 transcript. AI assistance was used for research organization, drafting, and validation. Bill status and the RealPage case page were last checked on July 27, 2026.