The New York City Council is scheduled to vote November 12 on a bill that would bar businesses from using a person's data and an algorithm to set an individualized price. The proposal, Introduction 891-A, targets a pricing process that can be invisible to a customer: two people may see different amounts for the same product or transaction because a system has inferred what each might be willing or pressured to pay.
The measure is narrower than a ban on all variable pricing. The Council's description preserves bona fide, transparent discounts and rewards, including loyalty offers, buy-one-get-one promotions and discounts for students or veterans. The distinction is whether a price difference is openly tied to an understandable program or instead generated from personal information that the buyer may not know is being used.
Enforcement would rest with the Department of Consumer and Worker Protection. The announced framework authorizes investigations and fines of as much as $1,000 for each violation, with each affected consumer or transaction treated separately. A corrective path for small businesses would allow a prompt remedy to avert a fine in qualifying circumstances. The final enacted text and any later agency rules will determine how those provisions operate in practice.
Personal data can include information collected directly, purchased from another company or inferred from behavior. A pricing model might combine browsing history, location, device information or account activity with a prediction about urgency or willingness to pay. The bill's significance is therefore partly technical: regulators will need a practical way to determine when an algorithm used personal data to produce a price rather than merely responding to inventory, time or market-wide demand.
Businesses also need a clear compliance boundary. A store can change a posted price for everyone, and the Council says ordinary transparent rewards remain permissible. The difficult cases arise when a platform runs experiments, assigns customers to unseen groups or draws on profiles assembled across services. Records describing model inputs, testing and price decisions could become important when a complaint cannot be evaluated from a customer's screen alone.
For consumers, the proposal addresses both affordability and disclosure. A personalized price can be hard to recognize because a buyer usually cannot see what another person was offered at the same moment. A complaint system will be more useful if it allows screenshots, receipts, timestamps and account conditions to be submitted without requiring customers to prove how a company's internal model worked.
The scheduled vote is one stage in the city legislative process. Passage by the Council would not by itself establish the effective date, final enforcement procedures or every definition that businesses must apply. The adopted bill, any mayoral action and subsequent rules should be checked before a company changes systems or a consumer assumes a particular transaction is covered.
The strongest implementation test will be whether the city can separate prohibited individualized pricing from legitimate public discounts without creating an easily avoided rule. Public guidance should explain covered data, prohibited uses, recordkeeping and the small-business correction process. Aggregate enforcement reports could then show the number of complaints, investigations, corrected practices and penalties without exposing consumers' personal information.
If enacted, the measure would place a direct consumer-protection limit on a commercial use of data rather than on data collection alone. It asks a concrete question of automated commerce: even when a system can estimate what an individual may pay, should a business be allowed to turn that estimate into a different price? The Council's vote will determine whether New York City answers that question through a local prohibition.
Primary sources: New York City Council surveillance-pricing announcement; Introduction 891-A legislative record. Reviewed November 12, 2026. Development: Council vote scheduled November 12, 2026.