As artificial intelligence (AI) and “big data” continue to shape modern commerce, lawmakers across the United States are increasingly turning their attention to surveillance pricing legislation. This emerging policy trend seeks to limit or ban the use of algorithms and automated decision systems that adjust prices based on consumer data such as browsing history, location, or even phone battery life. While none of these proposals have yet been enacted, they represent a growing interest in protecting consumers from pricing practices that rely on personal or device-specific data. Below is an overview of how California, Colorado, Georgia, Minnesota, and Pennsylvania have approached or are approaching the issue of surveillance pricing legislation in 2025.

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California: Fair Online Pricing Act

California’s SB 259, titled the Fair Online Pricing Act, would prohibit businesses from generating or displaying prices to consumers online based on certain data from the consumer’s device. The bill bars the use of “input data” such as the hardware or hardware state of the device, including battery life, performance characteristics, or device age, the presence or absence of any software, and geolocation data when used to make inferences about a consumer. Limited exceptions are provided for determining repair or maintenance costs, calculating trade-in values, or using location data to reflect legitimate regional price differences or real-time demand.

The legislation defines terms including “coupon,” “discount,” “hardware state,” and “online device,” applying broadly to connected devices such as smartphones and computers. It allows businesses to continue offering public discounts that do not rely on restricted data. SB 259 also clarifies that its provisions complement existing consumer protection laws and that cellular broadcast technology, which does not use individualized location data, is excluded from its scope.

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Colorado: Prohibiting Surveillance-Based Price and Wage Discrimination

Colorado’s HB 25-1264 proposed broad restrictions on using “surveillance data” to determine individualized prices or wages. The bill defined surveillance data as information derived through observation, inference, or surveillance related to personal characteristics, behaviors, or biometrics. Businesses would have been barred from using automated decision systems, including artificial intelligence and machine learning, to set different prices or pay rates based on this data.

The measure included exceptions for legitimate cost-based price differences and for discounts offered uniformly to specific groups such as veterans or seniors. Insurers and credit decisions under the Fair Credit Reporting Act were also exempt. The Colorado Attorney General and district attorneys could have imposed penalties up to $10,000 per violation, while individuals could pursue civil damages. Although comprehensive, the proposal did not advance out of committee, reflecting the early and exploratory stage of Colorado’s surveillance pricing legislation efforts.

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Georgia: Addressing AI-Driven Price and Wage Discrimination

Georgia’s SB 164 also sought to prohibit “surveillance-based price discrimination” and “surveillance-based wage discrimination.” The proposal would have made it unlawful for businesses to use automated decision systems or AI tools to set individualized prices or wages based on data gathered through observation or inference about a consumer’s behaviors, personal traits, or biometric identifiers.

The bill provided exceptions for legitimate cost variations and for insurers relying on risk-relevant data. Employers using AI to determine wages would have been required to disclose in plain language what data was considered and how it influenced pay decisions. Enforcement authority was given to the Georgia Attorney General, with civil penalties of up to $10,000 per violation and a private right of action for affected individuals. Although SB 164 did not move forward in 2025, it marked Georgia’s first major attempt to introduce surveillance pricing legislation targeting algorithmic decision-making in trade practices.

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Minnesota: Prohibiting AI-Based Dynamic Pricing

Minnesota’s HF 2452 and SF 3098, companion bills introduced in 2025, sought to prohibit the use of artificial intelligence to dynamically set product prices in real time. The legislation would have created Minnesota Statutes §325F.997, banning AI systems from adjusting or controlling prices based on market demand, competitor pricing, inventory levels, or consumer behavior.

By targeting what is often referred to as “dynamic pricing” or “algorithmic pricing,” the bills represented a narrower form of surveillance pricing legislation, focusing specifically on AI tools rather than all automated decision systems. Enforcement authority was assigned to the Minnesota Attorney General under existing consumer protection law. Neither bill advanced beyond committee review during the 2025 session.

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Pennsylvania: Proposed Ban on Rideshare Surveillance Pricing

In Pennsylvania, Representatives Andre Carroll and Ben Waxman circulated a cosponsor memorandum announcing their intent to introduce legislation banning “surveillance pricing” by rideshare companies. The forthcoming proposal, Banning Surveillance Prices by Rideshare Companies, would prohibit rideshare companies from using consumer device data, such as battery life, the number of wireless connections, or device age, to determine ride prices. Unlike surge pricing, which reflects real-time market demand, this proposal focuses on preventing pricing decisions based on private or technical information from a user’s phone. Although still in the memo stage, the initiative marks Pennsylvania’s entry into the national discussion on surveillance pricing legislation, emphasizing consumer privacy and fair pricing in the digital economy.

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