Concerning limiting the use of intimate personal data to make inferences that impact a person's financial position.
HB26-1210 would prohibit businesses from using “surveillance data” and automated decision systems to set individualized prices for consumers or individualized wages for workers. The bill defines surveillance data broadly to include information obtained through observation, inference, or surveillance about personal characteristics, online behaviors, or biometrics, and it defines price- or wage-setting algorithms as systems that use statistical modeling, data analytics, artificial intelligence, or similar techniques as a substantial factor in setting prices or wages.
The bill creates a general ban on individualized price setting and individualized wage setting, while listing several exceptions. These exceptions include pricing based on actual cost differences, time-based or supply-and-demand fluctuations, publicly disclosed discounts, loyalty or rewards programs, certain insurance pricing practices, customer-service credits and refunds, need-based medical discount programs, recurring subscription pricing not informed by a PWSA, and certain credit or financial-transaction decisions based on consumer reports or required application data. For wages, the bill allows individualized wages only when based solely on worker-specific data tied directly to the work performed and when the employer provides plain-language disclosures about the data used and how the algorithm uses it.
The bill also requires any person using a price or wage setting algorithm to publish reasonable procedures to ensure data accuracy, allow workers to request information about the data and how it is used, and permit workers to correct or challenge inaccurate data. It authorizes the attorney general to adopt rules and makes a violation a deceptive trade practice under the Colorado Consumer Protection Act. Enforcement may be brought by the attorney general or a district attorney, and aggrieved persons may sue for injunctive relief, damages, costs, and attorney fees.
The bill’s impact on state law would be to add a new part to Title 6 governing unfair and deceptive trade practices, specifically targeting algorithmic pricing and wage-setting practices. It would expand consumer protection law to cover the use of intimate or surveillance-derived data in pricing and compensation decisions, while also creating compliance obligations for businesses, employers, insurers, and platform-based or AI-assisted decision systems. It would also give regulators and private parties new enforcement tools and remedies.
The general sentiment appears to have been supportive among the bill’s sponsors and backers, as reflected by the broad bipartisan-style sponsorship list and the bill’s movement through committee before ultimately being vetoed by the governor. No committee transcript or recorded vote details were provided, so there is no direct evidence of floor debate in the materials supplied. The main points of contention likely centered on the breadth of the ban, the use of AI and data analytics in commercial pricing and compensation, and whether the bill would overreach into legitimate business practices such as discounts, insurance underwriting, loyalty programs, and pay-setting tools used for compliance and performance management.
The bill would amend Colorado’s deceptive trade practices statute by adding a new prohibition on individualized price and wage setting using surveillance data and by making violations actionable under the Colorado Consumer Protection Act. It would create new definitions for surveillance data, personal characteristics, online behaviors, biometrics, price or wage setting algorithms, and related terms, and it would impose disclosure and procedural requirements on entities that use such systems. The bill would affect businesses, employers, insurers, and other entities that use AI or data-driven tools in pricing, compensation, or related decision-making, while preserving specified exceptions for lawful discounts, insurance practices, and certain credit-related decisions.
The available context suggests the bill was framed as a consumer- and worker-protection measure aimed at limiting intrusive data-driven discrimination in pricing and pay. The broad sponsor list indicates substantial legislative interest, and the bill advanced through the House before the governor vetoed it. Because no committee transcripts or vote tallies were provided, the record here does not show detailed debate, but the veto implies at least some executive concern about the policy’s scope or implementation. Overall, the sentiment appears supportive among sponsors and aligned with privacy/fairness concerns, but contested at the executive level.
The likely points of contention are the bill’s broad definition of surveillance data and its restriction on algorithmic pricing and wage-setting, which could be viewed as reaching common business practices and AI-driven tools. Opponents may have been concerned about compliance burdens, litigation exposure, and uncertainty for dynamic pricing, compensation systems, insurance underwriting, and loyalty or discount programs. Supporters likely emphasized preventing discrimination, protecting privacy, and ensuring transparency in automated decision-making. The bill’s many exceptions suggest an effort to address these concerns, but the veto indicates unresolved disagreement over whether the balance struck was workable.