Purpose

This bill aims to protect consumers from certain price practices by large food retailers. It would prohibit pricing methods that use personal data or surveillance to tailor prices, and it would ban data collection and pricing based on sensitive characteristics. The overall goal is to prevent discriminatory or predatory pricing in big food stores.

Main provisions

• Definitions (key terms the bill uses)
  • Algorithm: a computational process that follows rules to determine a sequence of actions, including but not limited to artificial intelligence and facial-recognition software.
  • Consumer: a person buying goods for personal or household use.
  • Consumer data: information that identifies or could be linked to a specific device.
  • Electronic shelving labels: digital or wireless price displays.
  • Food retail establishment: a store above certain size thresholds that primarily sells household food items.
  • Personalized algorithmic pricing: pricing for a specific consumer or group set by an algorithm using consumer data, which can vary among consumers or groups.
  • Protected class data: information about characteristics protected by law (like ethnicity, age, disability, sex, sexual orientation, gender identity/expression, pregnancy outcomes and reproductive health care).
  • Surveillance pricing: offering or setting a price for a good for a specific consumer or group based on consumer data gathered through surveillance tech (such as sensors, cameras, device tracking, biometric monitoring, etc.).
• Prohibited practices (Predatory pricing prohibited)
  • A food retail establishment must not:
  • Use electronic shelving labels to implement pricing in the prohibited way.
  • Engage in personalized algorithmic pricing or surveillance pricing.
  • Collect data on individuals under 17 years old.
  • Use protected class data to set, offer, market, or sell a good if doing so either (a) withholds or denies an accommodation, advantage, or privilege, or (b) results in a different price for the good based on that data.
• Exclusions
  • Discounts, promotional pricing, and loyalty program benefits based on prior purchases are not prohibited.
  • The text also contains carve-outs related to financial services (e.g., banks, credit unions, mortgage-related entities) and insurers, indicating rules in this section do not apply to those sectors in the ways described.

Significant changes to existing law

• New prohibition in Minnesota Statutes (chapter 325D) creating a specific rule against surveillance pricing, personalized algorithmic pricing, and the use of consumer data or protected-class data to set prices in large food retailers.
• Introduces a defined set of terms (algorithm, consumer data, electronic shelving labels, surveillance pricing, etc.) to govern pricing practices in food retail establishments above certain size thresholds.
• Establishes size-based criteria to determine which retailers are covered (two thresholds: stores over 10,000 square feet; or stores over 85,000 square feet with at least 10% of sales floor devoted to food).
• Creates a framework that could require retailers to adjust or cease certain data-driven pricing practices, with potential implications for consumer protection and anti-discrimination enforcement.

Relevant Terms - surveillance pricing - personalized algorithmic pricing - algorithm - electronic shelving labels - consumer data - protected class data - food retail establishment - predatory pricing - price discrimination - discounts - loyalty programs - minors (under 17) - discriminatory pricing - biometric monitoring - AI / artificial intelligence - facial recognition software - dynamic pricing