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State Lawmakers Introduce New Wave of Personalized Algorithmic Pricing Bills

By Lindsey Tonsager, Jayne Ponder & Natalie Maas on March 26, 2026
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U.S. state lawmakers have introduced more than 40 bills across at least 24 states to regulate personalized algorithmic pricing in 2026 thus far, already outpacing the number of personalized algorithmic pricing bills introduced in all of 2025.  While their definitions and scope vary, the 2026 bills broadly refer to “personalized algorithmic” or “dynamic” pricing as the practice of setting or adjusting prices by analyzing consumer data through AI or other automated tools, which may result in different prices being offered to different consumers for the same good or service.  

If enacted, these bills could impose a broad range of restrictions on such pricing, including disclosure requirements, general prohibitions, sector-specific restrictions, and restrictions on the use of protected class data in pricing decisions.  Although the proposals vary significantly, a few key themes emerge across these bills:

  • Disclosure Requirements. Several states have introduced legislation that would require businesses to affirmatively disclose when prices are determined using algorithmic methods, similar to New York’s 2025 Algorithmic Pricing Disclosure Act.  For example, Connecticut SB 4, Maryland HB 1475, and several other state bills would require any person that establishes a price using “personalized algorithmic pricing” to include a disclosure stating that “THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA.”  

By contrast, Illinois’s Algorithmic Pricing Transparency Act (HB 4248) would take a more granular transparency approach, requiring entities that sell goods or services through online platforms to, among other obligations, provide disclosures of the “categories of personal data used to generate the price” and a linked explanation of the entity’s algorithmic pricing practices.  HB 4248 also would establish a consumer right to opt out of “surveillance pricing” and require entities to provide a “non-personalized baseline price” upon request.

  • General Prohibitions. Other proposals would categorically restrict or ban personalized algorithmic pricing practices to set or adjust consumer prices.  Note that these bills generally would not treat non-discriminatory discounts, coupons, or loyalty programs as covered pricing activity.  Vermont S.207 and California AB 2564, for example, would prohibit the use of “surveillance pricing”—defined as setting a customized price using personally identifiable information gathered through “electronic surveillance technology”—unless the price difference is based solely on cost differences or reflects a discount offered to all consumers on equal terms.  

Other bills, such as Washington’s Fair Pricing and Transparency Act (HB 2481 / SB 6312), would also prohibit pricing based on an “algorithmic determination of willingness to pay,” while bills like Rhode Island H 7849 would prohibit “algorithmic price increases” based on a consumer’s personal data, while exempting “price decreases.”

  • Protected Class Data. Many of these bills would impose restrictions on the use of protected class data for personalized algorithmic pricing decisions.  New Jersey A4085 / S3612, for example, would prohibit businesses from using “personalized algorithmic pricing, surveillance pricing, or any pricing strategy” based on “protected class data,” while Nebraska’s Protecting Consumers and Jobs from Predatory Pricing Act (LB 1006) and other state bills would generally prohibit any use of “protected class data” to set prices that results in discriminatory pricing outcomes, including the withholding or denial of accommodations or a price that differs from prices offered to other individuals or groups.  
  • Minor Data Limitations. Other bills would restrict the use of minors’ data for personalized algorithmic pricing.  Iowa SF 2278 and Tennessee HB 2052 / SB 1998, for example, would prohibit the collection or use of “data belonging to minors” under 17 years of age for “personalized algorithmic pricing,” regardless of parental consent.
  • Retail and Grocery Restrictions. Several bills would impose sector-specific limits on grocery stores and food retailers.  For example, Georgia’s Surveillance Pricing Act (HB 1439) and New Jersey S3732 would prohibit “retail food establishments” or “food retailers” from using “surveillance pricing” to set food or grocery prices.  Other bills like Oklahoma HB 3959 and Tennessee HB 2052 / SB 1998 would combine pricing restrictions with technology bans, prohibiting food retail establishments from using electronic shelf labels or digital shelf display technology. 

Taken together, these state personalized algorithmic pricing proposals reflect only one dimension of broader state AI legislative activity underway in 2026. 

Tags: AI
Photo of Lindsey Tonsager Lindsey Tonsager

Lindsey Tonsager is a recognized leader in representing companies before federal and state regulators, and is renowned for advising on minor protection, AI, and state comprehensive privacy laws.

Lindsey chairs the firm’s global Data Privacy and Cybersecurity practice. She advises clients in their…

Lindsey Tonsager is a recognized leader in representing companies before federal and state regulators, and is renowned for advising on minor protection, AI, and state comprehensive privacy laws.

Lindsey chairs the firm’s global Data Privacy and Cybersecurity practice. She advises clients in their strategic and proactive engagement with the Federal Trade Commission, the U.S. Congress, the California Privacy Protection Agency, and State Attorneys General on proposed changes to data protection laws, and regularly represents clients in responding to investigations and enforcement actions involving their privacy and information security practices.

Lindsey’s practice focuses on helping clients launch new products and services that implicate the laws governing the use of artificial intelligence; data processing for robotics, autonomous vehicles, and other connected devices; biometrics; online advertising; the collection of personal information from children, teens, and students online; e-mail marketing; disclosures of video viewing information; and new technologies.

Lindsey also assesses privacy and data security risks in complex corporate transactions where personal data is a critical asset or data processing risks are otherwise material. In light of a dynamic regulatory environment where new state, federal, and international data protection laws are always on the horizon and enforcement priorities are shifting, she focuses on designing risk-based global privacy programs for clients that can keep pace with evolving legal requirements and efficiently leverage the clients’ existing privacy policies and practices. She conducts data protection assessments to benchmark against legal requirements and industry trends and proposes practical risk mitigation measures.

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Photo of Jayne Ponder Jayne Ponder

Jayne Ponder counsels companies on the intersection of privacy, AI, and emerging technology regulation, and routinely represents clients in regulatory inquiries, investigations, and enforcement matters before federal and state agencies.

Jayne counsels clients across industries to launch and enhance products, services, and governance…

Jayne Ponder counsels companies on the intersection of privacy, AI, and emerging technology regulation, and routinely represents clients in regulatory inquiries, investigations, and enforcement matters before federal and state agencies.

Jayne counsels clients across industries to launch and enhance products, services, and governance programs involving their collection and use of data and emerging technologies. Her experience spans U.S. comprehensive privacy, automated decisionmaking, AI governance, biometric privacy, surveillance and algorithmic pricing, and AI transparency, disclosure, and safety frameworks. She partners with clients to design products and governance programs that keep pace with the dynamic regulatory environment, including in connection with digital and online advertising, social media, AI-powered and agentic services, connected devices, and robotics.

In addition, she advises companies on engagement with federal and state regulators, including through enforcement and rulemaking. Jayne analyzes privacy and security risks in complex corporate transactions. She also provides strategic input on the legislative, regulatory, and policy developments shaping the privacy and AI landscape.

Jayne maintains an active pro bono practice, focusing on assisting nonprofits with their privacy programs and elder estate planning.

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Photo of Natalie Maas Natalie Maas

Natalie is an associate in the firm’s San Francisco office, where she is a member of the Food, Drug, and Device, and Data Privacy and Cybersecurity Practice Groups. She advises pharmaceutical, biotechnology, medical device, and food companies on a broad range of regulatory…

Natalie is an associate in the firm’s San Francisco office, where she is a member of the Food, Drug, and Device, and Data Privacy and Cybersecurity Practice Groups. She advises pharmaceutical, biotechnology, medical device, and food companies on a broad range of regulatory and compliance issues.

Natalie also maintains an active pro bono practice, with a particular focus on health care and reproductive rights.

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  • Posted in:
    Technology and AI
  • Blog:
    Inside Privacy
  • Organization:
    Covington & Burling LLP
  • Article: View Original Source

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