HB0148 would prohibit “surveillance-based price setting” and “surveillance-based wage setting” in Maryland. The bill defines those practices as using surveillance data together with an automated decision system to set or inform customized prices for goods or services, or customized wages for employees or groups of employees. It also defines key terms such as “surveillance data” and “automated decision system,” and it expressly excludes passive computing infrastructure from the definition of automated decision system.
On the consumer side, the bill adds a new unfair, abusive, or deceptive trade practice under the Maryland Consumer Protection Act for violations of the new prohibition. It allows customized pricing in limited circumstances, such as when price differences reflect actual cost differences or when a discount or reward is offered uniformly to all consumers who meet disclosed eligibility criteria, including seniors, students, teachers, veterans, or military members, so long as eligibility is based on information voluntarily provided by the consumer. It also restricts the use of surveillance data obtained for a permitted customized price from being reused for profiling, targeted advertising, or other pricing purposes.
On the employment side, the bill bars employers from using surveillance-based wage setting and creates a complaint-based enforcement process through the Commissioner of Labor and Industry. The Commissioner may investigate upon an employee complaint, attempt informal mediation, or refer the matter to the Attorney General, who may seek injunctive relief, damages, or other relief. The bill also requires disclosure, in plain language, to employees hired under wage-setting systems that use automated decision systems, describing what data is considered and how it is used.
The bill’s impact would be to add new restrictions to Maryland commercial and labor law governing the use of artificial intelligence, data analytics, and surveillance-derived information in pricing and compensation decisions. It would amend the Maryland Consumer Protection Act and the Labor and Employment Article, while also cross-referencing the state’s existing artificial intelligence definition in the State Finance and Procurement Article. The bill takes effect October 1, 2026.
Because there are no committee transcripts or recorded votes provided, the available context does not show formal support or opposition. Based on the bill text, the measure appears aimed at consumer privacy, algorithmic fairness, and transparency in AI-driven pricing and pay practices. Likely points of contention include whether the bill is sufficiently narrow to allow legitimate dynamic pricing and compensation systems, how burdensome the disclosure requirements may be for employers and businesses, and whether the definitions of surveillance data and automated decision systems are broad enough to capture common business practices.
HB0148 would create new prohibitions in the Commercial Law Article and Labor and Employment Article against using surveillance data and automated decision systems to set individualized prices or wages. It would make violations of the pricing prohibition an unfair, abusive, or deceptive trade practice enforceable under the Maryland Consumer Protection Act, and it would establish a separate labor-law enforcement mechanism for wage-setting violations through the Commissioner of Labor and Industry and the Attorney General. The bill also adds statutory definitions and limited exceptions for cost-based pricing, disclosed discount programs, and certain employee-specific wage factors, while requiring transparency when automated systems are used in hiring or compensation.
No committee testimony or vote history is provided, so there is no recorded public sentiment in the supplied materials. The bill’s structure suggests a policy goal of limiting opaque AI-driven pricing and pay practices, with an emphasis on privacy, fairness, and disclosure. The inclusion of exceptions for discounts, cost-based differences, and certain employee-specific wage factors indicates an attempt to balance consumer and worker protections with business flexibility.
The main likely points of contention are the scope of the prohibitions and the practical effect on businesses that use data-driven pricing or compensation tools. Businesses may argue that the bill could restrict legitimate dynamic pricing, location-based pay, or incentive programs, while supporters are likely to emphasize concerns about surveillance, discrimination, and hidden algorithmic decision-making. Another possible dispute is whether the disclosure and enforcement provisions are sufficiently clear and workable, especially for employers and retailers using automated systems in routine operations.