Maryland and Connecticut Pass First State Laws Restricting AI Surveillance Pricing

First state laws target algorithmic price discrimination

Maryland and Connecticut have become the first states to enact laws restricting surveillance pricing—the practice of using AI and behavioral data to charge different consumers different prices for identical products. New York and New Jersey have passed similar legislation now awaiting gubernatorial signatures, according to an opinion piece in The Hill.

The laws vary in scope. Maryland and New Jersey focus primarily on grocery stores, while Connecticut extends restrictions more broadly to retail transactions. New York's proposal would apply across industries. All four states share concern that AI could shift pricing from traditional supply-and-demand dynamics to individualized predictions about what specific consumers will pay.

Maryland's law defines dynamic pricing as varying prices within a business day based on demand or other factors, including AI systems that recalibrate prices in near real time. The legislation also broadly defines "surveillance data" to include information collected through sensors, cameras, device tracking, biometric monitoring, and other technologies gathering personally identifiable information about consumer behavior, location, or characteristics.

Why it matters

These laws represent the first regulatory response to a fundamental shift in how prices could be determined in digital commerce. Unlike traditional dynamic pricing that responds to market-wide conditions—airline tickets rising during holidays, hotel rates increasing for major events—surveillance pricing uses individual-level data to predict maximum willingness to pay. For low-income Americans, this threatens to automate and scale existing economic inequities at unprecedented speed and invisibility.

The automation of economic inequality

The legislation addresses what Danielle A. Davis Canty, senior advisor and director of technology policy at the Joint Center for Political and Economic Studies, describes as the potential automation of the "poor tax"—the higher costs financially vulnerable communities already pay through predatory lending, overdraft fees, and limited access to affordable goods.

Modern AI systems don't need direct income data to infer purchasing power. They can analyze purchase histories, browsing activity, location data, loyalty programs, device information, shopping frequency, and responses to previous price changes. Collectively, these signals create detailed profiles capable of predicting consumer behavior and price sensitivity.

The concern intensified after public backlash to Walmart's discussions around electronic shelf labels and Delta Air Lines' comments about AI-driven pricing. Both companies later clarified their policies, but the reactions reflected growing unease about behavioral data shaping economic decisions behind the scenes.

Enforcement challenges ahead

Maryland has acknowledged that enforcing its new law may require specialized technical expertise many regulatory agencies lack. Investigators may need to examine machine learning systems, behavioral analytics, predictive algorithms, consumer profiling systems, and proprietary software recalibrating prices continuously in near real-time.

This creates challenges distinct from traditional consumer protection enforcement. With AI, decision-making processes may be embedded in automated systems, machine learning models, third-party data brokers, and predictive analytics tools operating at speeds consumers cannot access or understand.

For Black American households, which hold roughly 15 cents for every dollar in wealth that white households have, and other low-income Americans, the stakes are particularly high. Unlike traditional forms of unequal treatment, algorithmic pricing produces outcomes consumers may never see—they encounter only the final price, without knowing what information was collected, how it was analyzed, or whether another consumer received a different offer.

The details were first reported by The Hill in an opinion piece by Danielle A. Davis Canty.