Executive summary
Surveillance pricing—the practice of using AI and personal data to set individualized prices—has emerged as a major consumer protection issue. This brief examines the current policy landscape, compares legislative approaches across states, and analyzes how lawmakers from both parties are addressing this challenge. Surveillance pricing occurs when companies use data collected from consumers—such as location, browsing history, demographics, device type, purchase history, and behavioral patterns—to determine individualized prices for the same goods or services. Unlike traditional price discrimination (student discounts, senior pricing), surveillance pricing uses personal data to estimate each consumer’s maximum willingness-to-pay. Major news outlets have reported on surveillance pricing practices, raising public awareness and creating momentum for legislative action. States have adopted four primary strategies to address surveillance pricing, each with different tradeoffs between consumer protection and business flexibility. Democrats and Republicans frame algorithmic pricing concerns differently, leading to distinct legislative strategies. A data limitation approach requires defining which data categories should be restricted in pricing algorithms.
Surveillance pricing—the practice of using AI and personal data to set individualized prices—has emerged as a major consumer protection issue.
This brief examines the current policy landscape, compares legislative approaches across states, and analyzes how lawmakers from both parties are addressing this challenge.
Key Findings
- Legislative Explosion: 51 bills across 24 states in 2025 (up from 10 bills in 8 states in 2024)
- Bipartisan Interest: Both parties engaged, but with different framings and approaches
- Federal Activity: FTC study completed; DOJ pursuing algorithmic collusion cases
- State Leadership: New York first to enact disclosure law; Ohio has only Republican-sponsored bill
What is surveillance pricing?
Surveillance pricing occurs when companies use data collected from consumers—such as location, browsing history, demographics, device type, purchase history, and behavioral patterns—to determine individualized prices for the same goods or services. Unlike traditional price discrimination (student discounts, senior pricing), surveillance pricing uses personal data to estimate each consumer’s maximum willingness-to-pay.
| Finding | Details |
|---|---|
| Data Points Used | Retailers use at least 250 different data points to set prices |
| Tracking Methods | Mouse movements, items left in carts, time spent on pages |
| Data Sources | Precise location, browser history, purchase history, credit history, demographics |
| Industry Scope | Eight major companies investigated including Mastercard, JPMorgan Chase, McKinsey |
Source: page 4
Surveillance Pricing in the News
Major news outlets have reported on surveillance pricing practices, raising public awareness and creating momentum for legislative action.
| Outlet/Story | Key Finding | Significance |
|---|---|---|
| CBS News: Instacart Investigation | Prices vary up to 23% based on location and consumer data | Demonstrates real-world consumer harm |
| Walmart Digital Price Tags | Testing electronic shelf labels that can change prices remotely | Infrastructure for future surveillance pricing |
| FTC Report (Jan 2025) | Companies use 250+ data points for pricing | Federal validation of widespread practice |
Source: page 5
Four Approaches
States have adopted four primary strategies to address surveillance pricing, each with different tradeoffs between consumer protection and business flexibility.
| Framework | Approach | Business Impact | Consumer Protection | Examples |
|---|---|---|---|---|
| Prohibition | Ban surveillance pricing entirely | High—eliminates practice | Strong | CA AB 446 |
| Disclosure Only | Require notice to consumers | Low—allows practice | Weak | NY A3008 |
| Data Limitation | Restrict what data can be used | Moderate—targeted | Strong | GA SB 164; PA HB 1779 |
| Hybrid | Combine prohibition + disclosure | Moderate-High | Very Strong | IL SB 2255 |
Source: page 7
How Democrats and Republicans Differ
Democrats and Republicans frame algorithmic pricing concerns differently, leading to distinct legislative strategies.
| Dimension | Democratic Approach | Republican Approach |
|---|---|---|
| Core Framing | Consumer protection—companies exploit consumers | Antitrust/property rights—unfair market distortion |
| Legal Framework | New consumer protection regulations | Existing antitrust law + property rights |
| Primary Concern | Price discrimination harms vulnerable consumers | Data harvesting violates property rights; collusion distorts markets |
| Regulatory Philosophy | Proactive regulation to prevent harm | Minimal regulation; enforce existing law |
| Enforcement Preference | Strong private right of action | AG enforcement; targeted private action |
| Data Treatment | Prohibit use of specific data categories | Require consent; treat data as property |
| Business Impact Tolerance | Higher—willing to restrict practices | Lower—preserve business flexibility |
| Key Federal Champions | Sen. Klobuchar; Rep. Casar; Sen. Warren | Sen. Lee; Sen. Hawley; Sen. Grassley |
Source: page 8
Republican Perspectives
Senator Mike Lee (R-UT)
“Ownership should mean control. Americans deserve to decide who has access to their personal data and how it is used.”
Senator Josh Hawley (R-MO)
“We should give every single American the right to go to court and to sue these companies when they violate our property rights.”
Trump DOJ AAG Gail Slater
Algorithmic price fixing is “plain old collusion between competitors”—DOJ enforcement will increase.
Democratic Perspectives
Senator Amy Klobuchar (D-MN)
“Big corporations are using algorithms to raise prices and limit competition.”
Rep. Greg Casar (D-TX)
“You don’t have to be a progressive Democrat to say these gigantic corporations should not be spying on us.”
Areas of Bipartisan Agreement
| Area of Agreement | Policy Implication |
|---|---|
| Algorithmic Collusion is Problematic | Both parties support DOJ RealPage enforcement |
| Consumers Deserve Transparency | Disclosure requirements have bipartisan support |
| Some Data Use is Inappropriate | Protected class data should not be used for pricing |
| Consent Matters | Opt-in requirements align with property rights framing |
Source: page 12
State Legislative Activity
| State | Bill | Approach | Status |
|---|---|---|---|
| New York | A3008 | Disclosure requirement | Enacted May 2025; effective Nov 2025 |
Source: page 13
Notable Active/Recent Bills
| State | Bill | Sponsor (Party) | Approach |
|---|---|---|---|
| California | AB 446 | Democratic | Prohibition + Disclosure |
| Ohio | SB 328 | Republican (Sen. Blessing) | Antitrust approach |
| Illinois | SB 2255 | Democratic | Prohibition + Private right of action |
| Georgia | SB 164 | Democratic | Data limitation + Disclosure |
| Pennsylvania | HB 1779 | Democratic | Data limitation |
| Colorado | HB 1264 | Democratic | Prohibition (Vetoed) |
Source: page 13
Federal Activity
| Actor | Action | Status |
|---|---|---|
| FTC | 6(b) Study of surveillance pricing practices | Completed Jan 2025; public comment cancelled |
| DOJ | RealPage algorithmic collusion lawsuit | Active litigation; Trump DOJ continuing |
| Congress (D) | H.R. 4640 - Stop AI Price Gouging Act | Introduced July 2025 |
| Congress (Bipartisan) | Preventing Algorithmic Collusion Act | Reintroduced 2025 |
Source: page 14
Risk Framework
A data limitation approach requires defining which data categories should be restricted in pricing algorithms.
| Data Category | Risk Level | Policy Recommendation |
|---|---|---|
| Protected Class Data (race, gender, religion) | Highest | Prohibit—clearly discriminatory |
| Biometric Data (facial geometry, fingerprints) | High | Prohibit—invasive, discrimination risk |
| Genetic Information | High | Prohibit—health discrimination risk |
| Precise Geolocation (real-time tracking) | High | Prohibit—proxy for race/income |
| Web Browsing History | Moderate-High | Prohibit—personal preferences should not affect price |
| Purchase History (first-party) | Moderate | Allow with consent + disclosure |
| Aggregate Demographics (zip code level) | Lower | Allow with restrictions |
Source: page 15
Policy Options for State Legislators
| Option | Advantages | Considerations |
|---|---|---|
| Full Prohibition | Strongest consumer protection; eliminates practice | High business impact; may face industry opposition |
| Disclosure Only | Low business impact; transparency | May be insufficient; consumers rarely change behavior |
| Data Limitation | Targeted approach; preserves business flexibility | Requires clear definitions; enforcement complexity |
| Data Limitation + Consent + Disclosure | Balanced approach; bipartisan appeal | More complex to draft; multiple compliance requirements |
Source: page 17
Key Sources
- FTC Surveillance Pricing Study: ftc.gov/news-events/features/surveillance-pricing
- Consumer Reports State Bill Tracker: Innovation: consumerreports.org
- Future of Privacy Forum Analysis: fpf.org/blog/a-price-to-pay-u-s-lawmaker-efforts
- Ohio SB 328: legislature.ohio.gov/legislation/135/sb328
- New York A3008: nysenate.gov/legislation/bills/2025/A3008
- H.R. 4640 Bill Text: congress.gov/bill/119th-congress/house-bill/4640
