The Price is Right: Responsible Uses of Personal Data in Pricing is a 19-page report published in April 2026 by the Future of Privacy Forum (Future of Privacy Forum (FPF)) and written by Jameson Spivack, the organization's Deputy Director for Artificial Intelligence. It is a practical guide on data-driven pricing — the use of personal data, market data, and machine learning to tailor retail prices in real-time or near-real-time based on consumer signals — covering the mechanisms involved, the US legal landscape as of April 2026, and seven best-practice recommendations for responsible algorithmic pricing.
The report frames its subject against a backdrop of what it describes as "increasing scrutiny from civil society, lawmakers, and enforcers" as personalized pricing has become common retail practice. It is distinct from the algorithmic collusion problem, which concerns competitor data sharing and is addressed in Algorithmic Pricing and Antitrust. This report addresses personalized pricing — individualized prices charged to individual consumers — and the associated privacy, discrimination, and fairness implications.
Types of data-driven pricing
The report distinguishes several mechanisms:
- Dynamic pricing — prices vary by demand, time, or availability (flights, hotels, ride-sharing).
- Personalized pricing — prices vary by individual consumer data (purchase history, location, loyalty status).
- Personalized discounts and offers — targeted promotions based on a consumer profile.
- Bundle pricing — personalized package configuration.
The central question the report poses is when an individualized price crosses from a reward (a loyal-customer discount) to exploitation (extracting maximum willingness-to-pay) or discrimination (charging more to lower-income zip codes).
US legal landscape (as of April 2026)
The report surveys existing law applicable to data-driven pricing:
- FTC Act Section 5 — deceptive pricing practices, including personalized pricing that deceives consumers about the "real" price.
- State consumer protection laws — price gouging in emergencies and bait-and-switch.
- Civil rights laws — disparate impact where pricing algorithms proxy race, gender, or national origin.
- CCPA/CPRA — consumer rights to know about data used in pricing decisions, and opt-out rights for certain data uses (see California CCPA Regulations (Title 11, Division 6)).
On emerging legislation, the report notes that multiple state bills target personalized pricing disclosure (see FPF — A Price to Pay: U.S. Lawmaker Efforts to Regulate Algorithmic Pricing), and that the New York Algorithmic Pricing Disclosure Act (New York Algorithmic Pricing Disclosure Act (NY S 3008)) requires disclosure of algorithmic pricing. An appendix provides a full table of US state legislation relating to data-driven pricing as of 2026.
Seven best-practice recommendations
The report develops seven recommendations, framed around building "trustworthy pricing practices" aligned with the NIST AI RMF (NIST AI Risk Management Framework (AI RMF 1.0)):
- Map and track all data used in pricing — sources, provenance, and purpose.
- Test for bias — audit all relevant datasets and pricing algorithms for disparate impact across demographic groups.
- Establish clear internal policies — define permitted data types and pricing use cases based on fairness, context, and consumer expectations.
- Provide clear consumer disclosures — explain how data informs pricing and when personal data affects offers.
- Ensure personalized discounts relate to real baseline prices — avoid "anchoring fraud" where a fake "original price" is manufactured.
- Implement stronger safeguards for essential products — heightened standards for groceries, utilities, housing, and healthcare.
- Align data use policies with pricing algorithm vendors — third-party liability and vendor due diligence.
Relationship to algorithmic pricing antitrust
The report addresses a different dimension of algorithmic pricing than Algorithmic Pricing and Antitrust. The antitrust concern runs from competitor data sharing to collusion to supracompetitive prices that affect all consumers equally. The data-driven pricing concern runs from personal data to individualized prices, where some consumers pay more than others, potentially based on demographics.
Both concerns can coexist in the same algorithm, since a pricing algorithm could both collude and discriminate. The FPF report provides the privacy and fairness regulatory frame, while the Ezrielev report provides the antitrust frame.
Provenance
The report is a single source. Its best practices are FPF's own recommendations rather than regulatory text, and its account of the legal landscape is FPF's interpretation rather than authoritative guidance, which is reflected in the page's medium confidence rating. The April 2026 publication date makes the survey current.
Relationships
- related: Algorithmic Pricing and Antitrust — companion topic; FPF addresses privacy/fairness dimension while Ezrielev addresses antitrust dimension.
- related: FPF — A Price to Pay: U.S. Lawmaker Efforts to Regulate Algorithmic Pricing — FPF's tracking of 51+ state algorithmic pricing bills in H1 2025.
- related: New York Algorithmic Pricing Disclosure Act (NY S 3008) — primary state disclosure law addressed in this report.
- related: California CCPA Regulations (Title 11, Division 6) — CCPA/CPRA data rights apply to pricing data.
- supports: Algorithmic Accountability and Bias Audits — adds personalized pricing to the accountability framework.
- related: Incentives or Obligations? The U.S. Regulatory Approach to Voluntary AI Governance Standards — same FPF policy team; NIST AI RMF as compliance baseline.
- related: NIST AI Risk Management Framework (AI RMF 1.0) — baseline framework for the seven best-practice recommendations.
- related: Future of Privacy Forum (FPF) — publisher.
- related: Tokenization of Real-World Assets (AI-Era Finance) — adjacent AI-era pricing/markets concept; different dimension (financial-market manipulation vs. consumer-facing pricing).