Hallucinated facts
Plausible-sounding citations that do not exist, fabricated dates, statute numbers that are not real.
| #▼ | Company◆ | SPX Score◆ | P / D / H / O | Conf | Flags |
|---|
A public accountability index tracking which companies use your personal data to decide your price. Same product. Same moment. A different number, just for you.
A different number, just for you. The price reads the device, the ZIP, the loyalty tier, and the search history before it tells you the cost.
It is not surge pricing. It is not time-of-day discounting. It is your price, for who you are. Location, browsing history, device, demographic inference, loyalty data, and dozens of behavioral signals feed pricing systems that quote a different number to a different shopper for the same product, in the same moment, on the same screen.
SPX scores the most consequential US companies on four public dimensions and a single composite tier from Low to Critical. Every score is traceable to a documented source. Every confidence rating is derived mechanically from the source mix, not assigned by hand. The goal is the same as climate disclosure or antitrust review: a public lens on a practice the public cannot otherwise see.
Composite scores across all 67 companies, binned by 5. The shape skews high: surveillance pricing is a top-heavy phenomenon at the moment, dominated by intermediaries with documented harm and consumer-facing platforms with deep behavioural data.
Major regulatory, legislative, and litigation milestones from 2022 to today. The arc of momentum is short and steepening; what was scattered academic concern in 2022 is now active enforcement.
Roughly 100 surveillance-pricing bills have been introduced across 30 states and DC in 2026. The map shows where each state sits on the path from no action to enacted law. Hover or tap a state for the latest status.
Surveillance pricing is a supply chain, not a feature. Collection, identity resolution, unification, enrichment, decisioning, surface, and closed-loop feedback. SPX scores companies at every layer.
Surveillance pricing is not theoretical. Each finding below is from a regulator, court, or peer-reviewed study. None is from the company that operated the system being studied.
Composite scores split companies into four tiers. The bar below shows how today's index actually distributes — not how a normal curve says it should.
The five companies with the highest composite SPX scores. These are the ones with the deepest combination of personalization capability, data intake, documented harm, and opacity.
See the full index →Average composite score by sector, weighted by the count of companies in each. Hover or tap to see member companies.
HB 895 signed into law by Governor Wes Moore. Bans food retailers and third-party delivery providers from using surveillance data to set per-customer prices. Applies to grocers 15,000 sqft or larger. Effective October 1, 2026.
Walmart announces full chain ESL rollout, scaling from 2,300 to all US stores. Two recently disclosed Walmart patents describe AI-driven systems that analyse purchasing patterns to suggest pricing adjustments. Walmart denies plans for personalised dynamic pricing.
The largest US landlord (about 950,000 units) settles a multistate AG action over RealPage-coordinated rent setting. Parallel DOJ proposed consent decree filed August 2025. Cortland, LivCor, Camden, Cushman & Wakefield, and Willow Bridge are co-defendants under the same framework.
Senators Luján and Merkley introduce a Senate companion that bans surveillance-based price setting in grocery and bans electronic shelf labels in food retailers larger than 10,000 sqft. Targets the same ESL infrastructure Walmart and Kroger are scaling.
The committee sends letters to major travel and platform companies asking for documentation of revenue management algorithms, use of consumer data in pricing, testing and experimentation practices, and internal communications describing pricing tools.
Reps. Casar and Tlaib file the first federal bill to ban surveillance-based price setting. Cited examples include airlines raising prices after a search for a family obituary, and rideshare apps paying drivers less after detecting a pawn-shop visit. UFCW backs the bill.
Attorney General Rob Bonta directs the California DOJ to send inquiry letters to grocers, hotels, and retailers with significant online presence. Frames surveillance pricing as a likely violation of the CCPA's purpose-limitation principle.
Colorado moves a bill prohibiting surveillance-based discrimination in pricing and wages. After clearing the House, the bill is voted out of a Senate committee. Effective date for bills enacted without a safety clause is August 12, 2026.
FTC 6(b) study, House Oversight investigation, H.R. 4640 Stop AI Price Gouging Act, Senate grocery surveillance pricing bill.
Maryland enacted, New York and California in motion, Colorado advancing, plus dozens more across 30 states and DC.
RealPage and Greystar settling, Yardi class action live, Live Nation pending, GoodRx, Grubhub, and Outlogic closed.
SPX is built for anyone trying to understand, cite, or push back against surveillance pricing. Pick the doorway that matches what you came for.
Six signals you are being personally priced, plus four moves you can make today.
Pick any two scored records and see composite, dimensions, evidence, and flags side by side.
Concrete steps for consumers, advocates, journalists, and industry insiders. State and federal pressure points.
Citation guidance, downloadable assets, embed snippets, key findings.
The index ranks 67 companies across 28 sectors and four dimensions. Every score is traceable.
Browse the index →Personalization depth, data intake breadth, consumer harm evidence, opacity. Filter by tier, sector, or evidence confidence. Click any row to open the full company brief with sources.
| #▼ | Company◆ | SPX Score◆ | P / D / H / O | Conf | Flags |
|---|
This is analysis, not measurement. Each dimension is scored 0–100 as informed judgment. The evidence base underneath is what makes the judgment defensible. Composite weights consumer harm 30%; personalization, data intake, and opacity at 25%, 25%, and 20%.
Surveillance pricing is the use of personal data (location, browsing history, device, demographics, inferred traits) to set an individualized price for a good or service. Not surge pricing. Not time-of-day discounting. Your price, for who you are. SPX scores the most consequential companies on four public dimensions. Every score is traceable to a documented source.
This is analysis, not measurement. Each dimension is scored 0–100 as informed judgment. The evidence base underneath is what makes the judgment defensible. Composite weights consumer harm 30%; personalization, data intake, and opacity at 25%, 25%, and 20%.
How individually priced is the output? Patent filings, job postings for personalized pricing or price-optimization ML, vendor disclosures, SEC filings on dynamic pricing.
How much consumer data feeds the price? Privacy policy language, data broker contracts, app SDK inventory, loyalty program scope, biometric signals, inferred traits.
Is there documented differential pricing? FTC 6(b) inclusion, class actions, state AG inquiries, congressional letters, academic audits, investigative journalism.
How hidden is the practice from the consumer? Whether pricing factors are disclosed, whether consumers can see their price vs. a reference, arbitration clauses.
Every evidence item is tagged by source authority. Confidence is derived mechanically from that mix.
FTC orders, DOJ filings, court records, SEC filings, state AG actions, congressional letters, peer-reviewed studies.
Investigative journalism (NYT, WaPo, Reuters, ProPublica, CNBC), academic working papers, company 10-K and earnings-call disclosures.
Trade press, advocacy analysis, privacy policies, SDK inventories, industry-standard practice observations.
High: 3+ Tier-A, or 5+ total w/ 1+ A. Medium: 1+ Tier-A, or 3+ B. Low: thin file.
Surveillance pricing is not one product. It is a seven-layer supply chain that runs in the time between your tap and the number on your screen. Each layer is its own market with its own vendors, its own data, and its own incentives. SPX scores companies at every layer, because cutting any layer cuts the chain.
None of those seven appears on the receipt. The merchant you bought from did not invent the price; the price is the output of an industrial supply chain. SPX scores all seven layers because that is where regulation, journalism, and consumer pressure can actually intervene. A grocery-store ban on personalised pricing reaches Layer 06; an FTC 6(b) order reaches Layer 05; a state location-data ban reaches Layer 01. Each layer is its own opportunity for accountability.
Layer-by-layer analysis explains why some interventions work and some don't. Maryland's Protection from Predatory Pricing Act prohibits the use of surveillance data at Layer 06 inside grocery stores. The DOJ-RealPage settlement remediates Layer 05 in multifamily rentals. The FTC's Outlogic settlement remediates Layer 01 for sensitive locations only. New York's pending One Fair Price Package would reach Layers 05 and 06 across grocery and pharmacy. None of these alone reaches every layer, which is why the index tracks them all.
Not every personalized price is the result of all seven layers. A small-business retailer running an A/B price test on a single landing page is operating Layer 03 and 05 only. A telecom retention department using tenure to set a renewal offer is using a single internal database, not the full pipeline. The seven-layer framing is a worst-case taxonomy of what is technically available, not a claim that every transaction uses every layer.
Last reviewed: April 30, 2026. This map is updated when a new vendor or layer becomes consequential to the public record.
Surveillance pricing depends on a lattice of upstream vendors most consumers have never heard of. The companies on this page do not set the prices you see. They sell the inputs — identity, location, household composition, browsing behaviour, transaction history — that the price-setters consume. SPX scores most of these vendors directly; the ones that aren't in the index yet are mapped here for context.
Sources: FTC enforcement records, Gartner market guides, EPIC analysis, vendor 10-K and S-1 filings, and the Cracked Labs and ProPublica investigations on consumer-data brokers.
The layer that translates an anonymous browser, device, or hashed email into a single per-person identifier downstream systems can act on. Without identity resolution, none of the other vendors can stitch their signals together.
Approximately 2.6 billion verified global IDs. Match keys span hashed email, mobile ad ID, cookie, IP, postal address, and CRM ID. Partners directly with Acxiom, Experian, and Oracle to enrich its graph and resell to retailers, banks, and adtech.
Unified ID 2.0 hashes a logged-in email and propagates a deterministic identifier across the open web. Now adopted by Disney, Paramount, and major retail-media networks. Replaces third-party cookies for cross-site tracking and pricing decisions.
Analyses billions of transactions a year across a global consortium. Identifies returning users that wipe cookies, switch browsers, or use private mode. Sold for fraud detection but the same fingerprint identifies returning shoppers for pricing.
Formerly Iovation. Combines device intelligence with TransUnion's consumer credit file. Sold for fraud and KYC, used downstream of risk-based pricing in fintech and insurance.
The layer that adds inferred income, household composition, life events, and trait segments to the resolved identity. This is where a ZIP code becomes an inferred income range and a wallet-share estimate.
Canonical largest US consumer-data broker. Thousands of attributes per record across hundreds of millions of US adults. Real ID feeds the LiveRamp graph; data is licensed to retailers, financial-services firms, and adtech intermediaries.
Same category as Acxiom: demographic, financial, and behavioural attributes layered on Experian's consumer credit file. ConsumerView and Mosaic segments are the productised audiences. Sells onboarding and activation services parallel to LiveRamp.
Built from acquisitions of Datalogix, BlueKai, and AddThis. Oracle announced the wind-down of its advertising business in 2024. The historic data-broker function has migrated to LiveRamp and other identity-resolution platforms.
The layer that captures where the device is. Location is one of the strongest pricing signals available and is collected through SDKs embedded in third-party mobile apps. The consumer typically does not know the SDK is there.
Subject of the FTC's January 2024 settlement — the first against a data broker over sensitive location data. Banned from selling location tied to medical facilities, religious organisations, and other sensitive sites. The Cyber Security Location dataset was publicly listed at $240,000 per year.
Pioneered point-of-interest data and now monetises mobile location via the Pilgrim SDK embedded in 500+ third-party apps. Adopted voluntary guidelines on sensitive locations after the Outlogic settlement, but remains a primary US location source.
SafeGraph supplies points-of-interest and visit data; Veraset is its data-licensing spinoff for bulk commercial and government sales. Mobility data tracks where populations of devices move at city scale.
SDK installed in 500+ apps with insights on 20 million-plus devices, per public marketing. Used for retail-store visit measurement and competitive analysis. Customers include landlords, retailers, and equity researchers.
Italy-founded location-data firm operating in the US market. Voluntarily restricted sensitive-location use after the FTC's broader 2024 enforcement.
The CDP is where first-party retailer data meets the resolved identity and the third-party append. The output is a real-time per-shopper profile that the price-setter or recommendation engine reads on every page request.
Real-time profile unification plus Einstein decisioning for predictive offers, recommendations, and price suggestions. Pricing scales as a percentage of gross merchandise value, aligning Salesforce revenue with executed pricing decisions at retailer clients.
Adobe Target runs A/B price experiments at enterprise scale. Sensei AI generates per-segment offer optimisation against Adobe Analytics signals.
Acquired by Twilio in 2020. Pipes events from web and app SDKs into a unified customer record and routes to downstream tools. Heavy adoption in mid-market e-commerce and SaaS.
Acquired by Rokt in 2025. Strength in real-time mobile event streams. AI capabilities expanded via Indicative and Vidora acquisitions.
Started in tag management; expanded into CDP with Tealium Predict ML. Strong enterprise footprint in financial services and retail.
The closed-loop layer. Same identity used to set the price is used to attribute the eventual purchase, refining the model for the next user. This is where a lot of the new growth in the surveillance-pricing economy is happening.
Kroger's wholly owned analytics arm. Loyalty-card-tied purchase data plus app, location, and inferred-trait signals. Powers Kroger Precision Marketing; the August 2024 Warren / Casey congressional letter applies to the data-monetisation infrastructure 84.51° operates.
Targets 150 million weekly shoppers across 4,600 stores plus e-commerce. Mapped to SKU-level. Sold via The Trade Desk DSP. The targeting graph is the same graph that pricing decisions read against.
Target's in-house retail media network. Built on Circle loyalty data. Targeting and attribution feed back into Circle pricing decisions.
35-plus-year intermediary between retailers, CPG brands, and shoppers. Consolidates POS, eCommerce, CRM, and media engagement into a 360° real-time shopper profile available for targeting and personalisation.
Coupon-network operator turned data broker. Decades of household-level grocery purchase history. Often paired with Inmar in grocery promotional pipelines.
Productises card-spend data into retailer-facing personalisation and pricing analytics. Named in the FTC's 2024 6(b) order on surveillance pricing intermediaries.
The matrix below maps which retailers and platforms in the SPX index are publicly known clients of the upstream vendors. Sourced from FTC 6(b) filings, vendor case-studies, and investigative reporting.
| Vendor (Layer) | Disclosed clients in the SPX index |
|---|---|
| Revionics (Decisioning) | Home Depot, Tractor Supply, Hannaford |
| Bloomreach (Decisioning) | FreshDirect |
| Task Software (Decisioning) | McDonald's, Starbucks |
| RealPage (Decisioning) | Greystar, plus DOJ co-defendants Cortland Management, LivCor (Blackstone), Camden Property Trust, Cushman & Wakefield, Willow Bridge Property |
| Yardi Systems (Decisioning) | 18 named property managers in the federal antitrust class action; full list in court filings |
| Fetcherr (Decisioning) | Delta Air Lines (3% of US domestic, scaling to 20%), plus Virgin Atlantic, Azul, Viva Aerobus, WestJet, Royal Air Maroc |
| 84.51° (Closed loop) | Kroger (wholly owned subsidiary; powers Kroger Precision Marketing sold via The Trade Desk DSP) |
| Mastercard Test & Learn / SessionM (Enrichment) | FTC 6(b) respondent; client list non-public but spans retail, grocery, and travel |
| JPMorgan Chase data services (Enrichment) | FTC 6(b) respondent; client retailers undisclosed |
This is not a complete inventory of the consumer-data broker industry. It is a map of the vendors that are load-bearing for surveillance pricing specifically — the ones whose products an SPX-tier company would call into during a price decision. EPIC, Cracked Labs, the FTC's 2014 study, and the Privacy Rights Clearinghouse maintain broader inventories. The Vermont and California data-broker registries are also useful for working the upstream supply chain on a state-by-state basis.
Last reviewed: April 30, 2026. New vendors and category shifts are added as the public record warrants.
If your price feels different, it might be. The infrastructure behind individualized prices is mostly invisible to the people paying them. Here are the signals that something more than supply and demand is setting your number.
Open the same flight, hotel, or app on your phone and on a laptop in a different browser. If the price is different and the inventory hasn't changed, the difference is you, not the product. iPhone users were charged more on average than Android users for the same delivery in New York City, per the city's 2023 study of food-delivery apps.
Sign in. Refresh the page. If the price moved, the difference is your account history, your past purchases, or your inferred willingness to pay. Loyalty programs are not pure discounts; they are a signal-collection layer that can also feed individualized pricing.
If your grocery store has digital labels (small black-and-white screens, often with the price displayed) instead of paper, the price can change as fast as the network does. Walmart is rolling these out to all US stores by end of 2026; Kroger is on the same trajectory. Today these labels are typically synced to weekly promotions, not personalised. The infrastructure to personalise them exists.
Rideshare and delivery apps quote a surge or service fee that does not match the actual operating conditions you can see (a clear-traffic afternoon, a restaurant that just told you they are not busy). The fee is reading your willingness to pay, not the operating reality. The 2023 New York City study documented this directly.
Airlines and ticket sellers commonly read your interest signal. If you've searched the same flight, hotel, or concert seat multiple times, the system can take that as evidence of urgency. If the price went up after you came back, the system noticed you came back.
If you are renting, especially in a multifamily building managed by a major operator, the asking rent may be coming from a software product that pools data across competing landlords. The Department of Justice has settled with RealPage and Greystar over this practice; the Yardi class action is ongoing. The renter cannot see the algorithm; the rent quote arrives as the landlord's own asking price.
This page is a starting point, not legal advice. If your case is consequential, contact a consumer-protection attorney.
Plain-language definitions of the terms surveillance pricing, advertising technology, and US data-privacy regulators use. Click any company link to jump to its full SPX brief.
If a term used elsewhere in SPX is not defined here, file a request and it will be added.
Everything you need to cite SPX or embed a finding in a story. Free to use under Creative Commons Attribution 4.0; please credit Anna R. Dudley and link to surveillancepricingindex.org.
The Surveillance Pricing Index scores 67 US companies on whether their data is setting your price. It is built on public sources only and maintained by Anna R. Dudley.
Recommended: Anna R. Dudley, Surveillance Pricing Index, May 2026, https://surveillancepricingindex.org/.
For specific company scores, link to the per-company page (e.g. https://surveillancepricingindex.org/#kroger).
To link to a specific company finding inside a story:
<a href="https://surveillancepricingindex.org/#realpage"> RealPage scored 87/100 by SPX </a>
Each company is scored 0–100 on four dimensions: personalization depth, data intake breadth, consumer harm evidence, and opacity. The composite weights consumer harm 30%, the others 25/25/20. Tier thresholds: Critical 75+, High 60-74, Moderate 40-59, Low <40. Confidence is derived mechanically from the source tier mix, not assigned by hand. Full methodology at surveillancepricingindex.org/methodology.
Anna R. Dudley · annardudley.com · Substack newsletter
If you find a number on this index that looks wrong, please send a correction. The error will be logged on the AI Accountability page, in public, and you will be credited.
SPX is a map. The work is what people do with it. Here are concrete moves anyone can make today.
The two federal bills both need committee markup. The relevant committees:
Constituent calls to members of those committees move the calendar. If you live in a state represented on either committee, your call is more leveraged than most.
This page is updated when a new pressure point becomes consequential.
Pick any two scored companies. Composite, dimension breakdown, evidence count, and regulatory flags appear in parallel for fast comparison.
Surveillance pricing is now an active enforcement area at the federal level, an active legislative area in 30 states, and an active litigation area in multifamily housing and live events. This page is a snapshot of where regulation, legislation, and litigation actually sit. It is not legal advice. Dates are public record. If a status has shifted since the last review, the AI Accountability log will note the correction.
July 23, 2024 to present. The Federal Trade Commission issued compulsory orders to eight intermediary firms (Mastercard, JPMorgan Chase, Accenture, McKinsey, PROS, Revionics, Bloomreach, Task Software) requiring disclosure of how consumer data feeds individualized pricing systems sold to retailers. The January 2025 staff perspective found these intermediaries worked with 250+ client businesses across retail, grocery, hospitality, and travel.
In April 2026 testimony before Congress, FTC leadership confirmed staff work on surveillance pricing continues and the agency is assessing whether additional disclosures may be required when pricing is highly personalized or driven by consumer data.
March 5, 2026. The House Oversight Committee formally launched an investigation into the use of surveillance pricing. Letters were sent to major travel and platform companies requesting documentation of revenue management algorithms, use of consumer data in pricing, testing and experimentation practices, and internal communications describing pricing tools and outcomes.
May 11, 2026. Ranking Member Frank Pallone Jr. (D-NJ) sent letters to 25 food retailers and pharmacies, including Albertsons, Stop and Shop, Amazon, Whole Foods, CVS, Target, Walgreens, Walmart, and Wegmans, asking how each company collects personal data and whether it uses that data to set prices. Responses are due May 26, 2026. The inquiry is the first congressional action since the Section 6(b) order to ask retailers directly, rather than the intermediaries who sell pricing software to them.
H.R. 4640, July 2025. Reps. Greg Casar (D-TX) and Rashida Tlaib (D-MI) introduced the first federal bill to ban companies from using AI to set prices or wages based on Americans' personal data. The bill prohibits Surveillance-Based Price Setting, defined as automated systems that set different prices for different consumers using personalised data including browsing history, location, address, race, gender, or genetics.
Specific examples in the bill text: an airline raising prices after seeing a search for a family obituary, a rideshare app paying a driver less after detecting a visit to a pawn shop. Backed by UFCW and a coalition of consumer-protection groups. No markup scheduled.
February 2026. Sens. Ben Ray Luján (D-NM) and Jeff Merkley (D-OR) introduced the Senate companion to a House bill from August 2025. The bill bans surveillance-based price setting in grocery stores and bans electronic shelf labels in food retailers larger than 10,000 square feet. Targets the same ESL infrastructure Walmart and Kroger are rolling out at scale.
HB 895 signed by Governor Wes Moore on April 28, 2026. Effective October 1, 2026. Maryland is the first state to enact a surveillance pricing prohibition.
The law bans food retailers and third-party delivery providers from using dynamic pricing to increase food prices for the same consumer. Applies to retailers with locations 15,000 sqft or larger that conduct substantial grocery operations selling tax-exempt food. Discounts, loyalty programs, and promotional pricing remain permitted.
S8616 / A9396 One Fair Price Package. Backed publicly by Attorney General Letitia James on March 16, 2026. Would prohibit personalized algorithmic pricing in New York, ban electronic shelf labels in large food and drug retailers, and create enforcement mechanisms with a private right of action.
May 8, 2026. Attorney General James held rallies in the Bronx and Syracuse calling on the legislature to pass the One Fair Price Act in the closing weeks of the 2026 session. The push frames surveillance pricing as a cost-of-living issue and asks for AG enforcement authority including civil penalties and restitution.
AB 2564, introduced February 20, 2026. Would prohibit retailers from engaging in surveillance pricing, defined as setting customized prices based on personally identifiable information collected through electronic surveillance technology, including data acquired from third parties. Civil penalties up to $12,500 per violation, three times that amount for intentional violations.
CA AG inquiry, January 27, 2026. Attorney General Rob Bonta launched an investigative sweep, directing the Department of Justice to send inquiry letters to grocers, hotels, and retailers with significant online presence. The inquiry frames surveillance pricing as a likely violation of the California Consumer Privacy Act's purpose-limitation principle.
HB 25-1264. Prohibits surveillance-based discrimination against consumers and workers through automated decision systems that use surveillance data to inform individualized prices or wages. Passed the House; voted out of a Senate committee in April 2026. Effective date for bills enacted without a safety clause is August 12, 2026.
Roughly 100 bills covering surveillance, algorithmic, and personalized pricing have been introduced across 30 states and the District of Columbia in 2026. Active jurisdictions outside the four highlighted above include Illinois, Massachusetts, New Jersey, Georgia, and Ohio. Trade associations track this as the fastest-growing state-level consumer-protection front of the year.
August 23, 2024 (complaint) and November 24, 2025 (proposed settlement). The Department of Justice and a coalition of state attorneys general alleged that RealPage's algorithmic rent-pricing software pooled nonpublic competitor data to coordinate rents across landlords. The proposed settlement requires RealPage to stop offering software that uses nonpublic competitively sensitive data shared among landlords, prohibits market surveys used to gather nonpublic competitive intelligence, requires retraining of models on compliant datasets within 180 days, and subjects the company to a court-approved monitor for ten years.
May 8, 2026. DOJ published its Response to Public Comments on the proposed Final Judgment in the Federal Register, defending the consent decree against public objections and clarifying the owner-inputted-data limits, the geographic-variable scope, and the override-recommendations requirement. This is the last Tunney Act step before court entry of final judgment.
RealPage did not admit liability. Final judgment is pending court entry.
May 8, 2026. California AG Rob Bonta, with the San Francisco, Los Angeles, Napa, and Sonoma district attorneys, secured a $12.75M CCPA settlement against General Motors and OnStar for selling names, contact information, geolocation, and driving-behavior data on hundreds of thousands of Californians to Verisk Analytics and LexisNexis Risk Solutions between 2020 and 2024. The two data brokers built driver-rating products marketed to auto insurers. The settlement is the largest CCPA penalty to date and the first state AG action to name Verisk and LexisNexis Risk as named purchasers of consumer driver data. GM must stop selling driving data to consumer reporting agencies for five years and delete retained driving data within 180 days absent express consent.
May 4, 2026. The Federal Trade Commission announced a proposed settlement banning mobile-location broker Kochava and its successor Collective Data Solutions from selling, sharing, or disclosing sensitive-location data without affirmative express consent. The order resolves the August 2022 FTC complaint alleging sale of precise location data on hundreds of millions of mobile devices including visits to sensitive venues. A first-of-its-kind provision requires Kochava to allow consumers to request the names of third parties that bought their precise location data.
Phillips, April 22, 2026, and Squire, May 1, 2026 (both E.D.N.Y.). The first U.S. surveillance-pricing class actions against an airline. Plaintiffs allege JetBlue.com tracked user browsing via FullStory's session-replay technology and fed those signals into PROS Holdings's pricing algorithm to set per-user fares without disclosure. The Squire complaint adds Virginia Consumer Protection Act and Virginia wiretap claims; both cases stack the Electronic Communications Privacy Act and state consumer-protection theories. The two suits effectively put session-replay vendors on notice that being named as a data recipient in a pricing pipeline carries litigation exposure.
August 2025 (DOJ proposed consent decree) and November 2025 ($7M multistate AG settlement). Greystar, the largest US landlord with about 950,000 units under management, agreed to stop using RealPage software that incorporates competitively sensitive competitor data, refrain from sharing pricing strategies with competing landlords, and accept a court-appointed monitor.
A parallel $7M penalty was secured by California AG Rob Bonta with eight other state AGs participating. Co-defendants under the same DOJ consent-decree framework include Cortland Management (April 2025), LivCor / Blackstone, Camden Property Trust, Cushman & Wakefield, and Willow Bridge Property Company.
Filed September 2023; motion to dismiss denied December 2024; narrowed April 2026. A federal antitrust class action in the Western District of Washington alleges that Yardi Systems and 18 property management firms orchestrated a nationwide rent-fixing scheme through Yardi's RENTmaximizer (now Revenue IQ) software. The complaint cites a test-run economic analysis finding an average 6 percent overcharge on units priced via the software. Judge Lasnik denied the motion to dismiss with the statement that "as technology has evolved, so too have methods of price fixing." The April 2026 ruling dismissed 10 out-of-state property managers for lack of personal jurisdiction; Yardi remains the lead defendant.
May 23, 2024. DOJ and 30 state attorneys general filed an antitrust complaint against Live Nation and Ticketmaster alleging that the company maintains monopoly power partly through pricing practices. The complaint is the highest-profile antitrust action in any direct consumer pricing market.
February 1, 2023. First-ever enforcement action under the FTC's Health Breach Notification Rule. GoodRx paid a $1.5M penalty for sharing health-related information with advertising platforms in ways that informed personalized offers. Established that health-adjacent surveillance flows can be reached under existing federal authority.
December 2024. $25M settlement covering deceptive pricing and undisclosed delivery fee practices. Settlement remedies imposed disclosure requirements that target the fee-stack opacity at the heart of the platform's consumer-facing pricing.
January 2024. First-ever FTC settlement against a data broker for selling sensitive location data. Outlogic was banned from selling location data tied to medical facilities, religious organizations, correctional facilities, domestic violence shelters, and political-activity sites. Establishes that bulk mobile-location feeds, a major Layer-1 input to surveillance pricing, can be reached under FTC unfair-and-deceptive-practices authority.
December 2024. Final rule on undisclosed fees in live-event ticketing and short-term lodging. While not a surveillance-pricing rule per se, it directly constrains a major opacity vector that surveillance pricing operates inside.
Last reviewed: May 13, 2026. If you spot a status change, file a correction and it will be added to the AI Accountability log.
SPX is built only on public sources. Every score is traceable to one or more documented references on the company’s detail page. The list below aggregates every distinct source cited across the index, grouped by source tier. 114 unique sources are in active use as of May 2026.
If you have a primary or reputable source we have missed, the index is designed to be updated. Email Anna and your evidence will be added to the next release.
FTC orders, DOJ filings, court records, SEC filings, state AG actions, congressional letters, peer-reviewed studies.
Investigative journalism, academic working papers, company 10-K and earnings-call disclosures.
Trade press, advocacy analysis, privacy policies, SDK inventories, industry-standard practice observations.
Every score, label, and source citation in this index was reviewed by a human before it shipped. The errors logged below were caught during that review. They are kept in public for the same reason climate scientists publish their model uncertainty: the only way to trust a number is to know how it was made.
If you find a number on this index that looks wrong, that is the experience this page is supposed to make possible. Tell us, and you will be added to the log.
An “error” in this log is anything an AI assistant produced that would have shipped wrong if a human hadn’t caught it. That includes:
Plausible-sounding citations that do not exist, fabricated dates, statute numbers that are not real.
An evidence item credited to the wrong outlet, a regulatory action assigned to the wrong agency or year.
A dimension score that does not match what the cited evidence supports, or a tier assignment that ignores the evidence mix.
A status that was true at the time of writing but has since been overtaken by enforcement action, settlement, or company disclosure.
A trade-press or advocacy item miscoded as Tier A, lifting confidence higher than the public record warrants.
Companies dropped from a filter without explanation, deep links that point to the wrong record.
This log starts with the build of v0.4. Pre-launch errors caught during prototype iteration are not retained — the policy of “write it down before fixing” starts now.
The first build loaded company data via fetch(‘./data/companies.json’). On the public site this was fine; previewing the file directly from disk in Chrome triggered a CORS block and the table rendered the empty-state error. Fixed by inlining the dataset as a <script type="application/json"> block inside the page so file:// review works the same as the deployed site.
The handoff prototype shipped with a warmer cream background (#f5efe3) and a slightly cooler forest green (#0f3b2d) than DCSI uses. Reviewing the live DCSI CSS surfaced the canonical values: stone #F4F2ED, forest #1B3A2D, bronze #A8885A. SPX now matches those exactly. Reason the prototype was off: the handoff doc claimed brand-correctness without verifying against the live sister tool.
v0.3 masthead linked to Home, DCSI, SPX, and Newsletter. Cross-tool links belong in the footer; the masthead is for sections of the current tool. Replaced with The Index, Methodology, Data Sources, AI Accountability, About.
Through the v0.4 release, SPX scored RealPage as a vendor but did not separately score Greystar (the largest US landlord at ~950,000 units, with a DOJ proposed consent decree filed August 2025 and a $7M multistate AG settlement in November 2025) or Yardi Systems (the closest peer pricing intermediary to RealPage, with a federal antitrust class action whose motion to dismiss was denied in December 2024 and narrowed in April 2026). Caught during the May 2026 review pass. Both added in v0.5 and the Policy & Law page extended with cards for Greystar, the broader DOJ landlord co-defendant cluster (Cortland, LivCor, Camden, Cushman & Wakefield, Willow Bridge), and the Yardi class action. Index size grew from 65 to 67 records.
The Policy & Law page previously stated "more than 40 related bills covering surveillance, algorithmic, and personalized pricing have been introduced across more than two dozen states." Updated 2026 tracker reporting puts the actual count at roughly 100 bills across 30 states and DC. Corrected in v0.5 with the sourced figure and a refreshed list of named active jurisdictions (Illinois, Massachusetts, New Jersey, Georgia, Ohio in addition to NY, CA, CO, MD).
The v0.4 Policy & Law page mentioned the Casar / Tlaib bill without bill number, status, or its actual provisions. Upgraded in v0.5 to reference H.R. 4640 (Stop AI Price Gouging and Wage Fixing Act), with the airline-obituary and pawn-shop examples drawn directly from the bill text, plus the UFCW endorsement. The Senate companion bill from Sens. Luján and Merkley was added separately as the Stop Price Gouging in Grocery Stores Act.
Surveillance pricing scores 67 companies on whether your data is setting your price. Every score is traceable to a public source. No corporate funding.
The Surveillance Pricing Index answers a question that should be simple: when you pay a price online, in an app, or at a shelf, is that price the same one your neighbor sees? SPX scores the most consequential companies on four public dimensions: personalization depth, data intake breadth, consumer harm evidence, and opacity. The four combine into a single tier from Low to Critical. Sister tool to the Data Center Stress Index.
Each company is reviewed across four dimensions, scored 0–100 based on public record. The composite score weights consumer harm 30%, personalization 25%, data intake 25%, and opacity 20%. Every score has an evidence base attached. Every evidence item is tagged Tier A (primary, e.g. FTC order, DOJ filing, peer-reviewed study), Tier B (reputable, e.g. major investigative journalism, 10-K disclosure), or Tier C (secondary, e.g. trade press, advocacy analysis, privacy policy). Confidence is then derived mechanically from the tier mix, not assigned by hand.
SPX does not measure the prices a given consumer paid. It does not generate legal findings. It does not name individuals at the companies it scores. It informs judgment. It does not replace it.
SPX is a project by Anna R. Dudley. It is a sister tool to the Data Center Stress Index. The entire tool is built using publicly available data and is published under a Creative Commons Attribution license. The purpose is to put the same kind of accountability lens on surveillance pricing that is already routine in environmental disclosure and antitrust.
For questions, speaking inquiries, or evidence submissions, contact Anna R. Dudley. Power moves before policy does.