Purpose

To curb certain pricing practices in large food retail establishments by prohibiting pricing that is based on consumer data, including data gathered through surveillance, and by restricting the use of algorithmic pricing that targets individuals or groups. The bill aims to prevent price discrimination and ensure fair pricing for shoppers.

What the bill would do (Main provisions)

• Prohibits surveillance pricing in large food stores: Stores over 10,000 square feet, and that dedicate at least 10% of their sales floor to food, cannot use pricing based on monitoring or collecting information about shoppers.
• Prohibits personalized algorithmic pricing: Stores must not set or offer prices based on an algorithm that uses individual or group consumer data to tailor prices.
• Prohibits electronic shelving label-based pricing tied to data: Stores cannot use electronic or wireless pricing displays to implement data-driven pricing targeted at shoppers.
• Prohibits collecting data on minors: Stores cannot collect data on individuals under 17 years old for the purpose of pricing.
• Prohibits use of protected class data for pricing: Stores cannot use information about protected characteristics (like race, ethnicity, age, disability, sex, sexual orientation, gender identity and expression, pregnancy outcomes, etc.) to set, offer, market, or sell a price if it results in withholding or denying an accommodation or otherwise charging different prices based on those traits.
• Prohibits price discrimination tied to protected class data: If using protected class data would lead to different prices for different individuals or groups, this would be prohibited.
• Exclusions for discounts and loyalty programs: The bill does not forbid standard discounts, promotional pricing, or loyalty benefits that are based on a shopper’s previous purchases.

Definitions (key terms defined in the bill)

• Algorithm: A computational process using rules to determine a sequence of operations, including AI systems and facial-recognition software.
• Consumer: A person buying goods for personal, family, or household use.
• Consumer data: Any data that identifies or could reasonably be linked to a specific device; excludes certain types of location data.
• Electronic shelving labels: Digital or wireless displays that show product and pricing information.
• Food retail establishment: A large store (over 10,000 square feet) that mainly sells household food items and dedicates at least 10% of its sales floor to food.
• Personalized algorithmic pricing: Pricing set by an algorithm using consumer data that can vary among consumers or groups.
• Protected class data: Information about a person or group that identifies legally protected characteristics (ethnicity, national origin, age, disability, sex, sexual orientation, gender identity and expression, pregnancy outcomes, reproductive health care, etc.).
• Surveillance pricing: A price set for a specific consumer or group based on consumer data gathered through electronic surveillance, including sensors, cameras, device tracking, biometric monitoring, or other observation/data collection methods about behavior, location, or personal attributes.

Exclusions and carve-outs

• Disclaimers on standard promotions: Nothing in this section prevents discounts, promotional pricing, or loyalty program benefits based on a consumer’s previous purchase history.
• Scope note: A portion of the bill indicates there are separate provisions related to financial services (e.g., banks, credit unions, mortgage originators, brokers/dealers, investment advisors) and insurers, suggesting a broader or additional set of rules beyond food retail pricing.

Potential impact and what changes from the status quo

• Adds explicit prohibitions on certain data-driven pricing practices in large grocery-like stores, aiming to prevent price discrimination based on personal characteristics and on data gathered from surveillance.
• Introduces defined terms (like surveillance pricing and protected class data) to guide enforcement and compliance.
• Creates a framework that distinguishes allowed routine discounts and loyalty programs from prohibited, data-driven pricing practices.
• Signals a broader concern with privacy in pricing and with algorithmic decision-making in retail.

Possible implications for businesses and consumers

• Businesses may need to adjust pricing systems to remove data-driven or surveillance-based elements and ensure pricing is not targeted by individual or group characteristics.
• Stores may need to implement compliance measures around data collection, data use, and disclosure related to pricing.
• Consumers could experience pricing that is more uniform across individuals, reducing potential discrimination in pricing.

Relevant Terms surveillance pricing personalized algorithmic pricing electronic shelving labels consumer data protected class data algorithm consumer food retail establishment predatory pricing discounts loyalty program price discrimination data-driven pricing minors (data collection prohibition) section 325D.141 (section referenced in the bill)