Purpose

This bill would add a new rule in Minnesota commerce laws to stop using automated decision systems and surveillance data to set individualized prices or wages. It aims to protect consumers and workers from discrimination based on information gathered through monitoring, tracking, or other observation, and it requires businesses to be transparent about the data they use.

Key Definitions (overview)

• Automated decision system: A computer-based system, software, or process that uses calculations to help or replace human decisions, including tools based on machine learning, statistics, or artificial intelligence.
• Surveillance data: Information gathered through observation or monitoring about a person, including personal characteristics, behaviors, biometrics, and other data related to a person or group.
• Surveillance-based price discrimination: Using an automated decision system to set individualized prices for a consumer based on surveillance data.
• Surveillance-based wage discrimination: Using an automated decision system to set individualized wages for a worker based on surveillance data.
• Consumer: An individual buying goods for personal or household use.
• Worker: A person who performs work for an employer (employee, independent contractor, etc.).
• Price: The total amount charged for a good or service, including related costs and terms.
• Wage: The terms of pay for labor, including hourly, salary, bonuses, scheduling, and other earnings-related terms.
• Biometric data: Data from fingerprints, voice, facial features, iris/retina scans, genetic information, etc.

Prohibited practices (Main Provisions)

• Subd.2 – Price discrimination: It is prohibited to use surveillance-based price discrimination.
  • The bill lists several allowed scenarios that are not considered surveillance-based price discrimination:
  • Price differences justified by actual costs to provide goods or services to different customers.
  • Discounts offered to all customers on equal terms, provided the terms are publicly available and accessible.
  • Discounts that reward membership in a clearly identified group (e.g., military members, veterans, teachers, students, seniors) under publicly available terms.
  • If an insurer uses only risk-relevant data in an automated decision system to set insurance prices, that activity may be allowed under specific conditions.
  • Other allowances: If a lender refuses to extend credit under terms based on data from a consumer report protected by the federal Fair Credit Reporting Act, that refusal is not considered surveillance-based price discrimination.
• Subd.3 – Wage discrimination: It is prohibited to use surveillance-based wage discrimination.
  • Allowed scenarios (exceptions):
  • Offering wages based only on data directly related to the individual worker’s tasks or on cost differences to provide labor.
  • Before hiring, clearly disclose to all workers what data the automated decision system uses and how it considers that data.
  • Clarification: Not allowed to use the surveillance data rule to justify not hiring someone who has not previously worked for the employer.

Publication and transparency (Subd.4)

• Employers and others who use an automated decision system for wages or prices must publish reasonable procedures to:
  • Ensure the accuracy of all data used by the system.
  • Allow workers or consumers to correct or challenge the data the system uses.
  • Provide information about what data is considered and how the automated decision system uses that data when setting specific prices or wages.

Significant changes and impact

• Creates a new statewide prohibition on surveillance-based price and wage discrimination, expanding protections for consumers and workers.
• Requires companies to publish data handling procedures and to be transparent about the data used in pricing and pay decisions.
• Introduces specific definitions (including automated decision systems and surveillance data) to guide enforcement and compliance.
• Affects businesses that use algorithms, machine learning, or other data-driven methods to set prices or wages, including insurers and lenders, by imposing stricter rules and disclosure requirements.

Practical implications for businesses and individuals

• Businesses using ADS must review their pricing and payroll practices to ensure they do not rely on surveillance data in a way that creates individualized prices or wages.
• They may need to adjust to the exceptions and ensure public, accessible terms for discounts or cost-based pricing.
• They should prepare to publish procedures, maintain data accuracy, and provide channels for data corrections and explanations to consumers/workers.

Relevant terms - automated decision system - surveillance data - surveillance-based price discrimination - surveillance-based wage discrimination - price - wage - consumer - worker - biometric data - personal characteristics - insurer - cost-based differential pricing - discounts - transparency - data accuracy - Fair Credit Reporting Act (FCRA) references - group/membership discounts - task-related data - pre-hiring disclosure