FILE – United Airlines passengers head to a security checkpoint at O’Hare International Airport, Friday, May 12, 2023, in Chicago. Consumers will pay more for that airline flight or for a room in a hotel during peak vacation times. But a big shopper backlash to media reports this past week that fast-food chain Wendy’s had plans to increase prices during the busiest time at its restaurants clearly showed that consumers don’t want basic food items like hamburgers and shakes going up and down. (AP Photo/Charles Rex Arbogast, File) Imagine two people shopping online for the exact same item at the exact same time, yet one receives a higher price because an algorithm predicts they are willing to pay it.
That possibility sits at the center of growing concerns around surveillance pricing and AI-driven personalized pricing systems — concerns serious enough that Maryland became the first state, and Connecticut the second, to enact laws restricting certain forms of surveillance pricing. That momentum has continued in New York and New Jersey, where lawmakers have passed similar legislation awaiting their governors’ signatures.
Although these efforts share a common goal, they differ in scope. Maryland and New Jersey focus primarily on grocery stores. Connecticut extends its restrictions more broadly to retail transactions, and New York’s proposal would apply across industries.
Together, these laws reflect a growing concern that AI could shift pricing from supply and demand to individualized predictions. Instead of asking, “What is this product worth?” AI asks, “What is this consumer willing to pay?”
For many Black Americans — whose “households have roughly 15 cents for every one dollar in wealth that White households have” — and other low-income Americans, that concern feels deeply familiar.
Low-income communities have long recognized what is often called the poor tax. Financially vulnerable communities frequently pay more over time — through predatory lending, overdraft fees, subprime financial products, higher insurance costs and limited access to affordable goods and services. The concern is now that AI could automate and scale those inequities, making them harder to detect.
These systems would not need to know a consumer’s income directly. Modern AI can infer purchasing power and consumer behavior from purchase histories, browsing activity, location data, loyalty program participation, device information, shopping frequency and responses to previous price changes. Individually, these signals may seem insignificant. Collectively, they create detailed profiles capable of predicting consumer behavior.
Unlike traditional forms of unequal treatment, however, algorithmic pricing can produce unequal outcomes that consumers may never see. Most people only encounter the final price. They do not know what information was collected, how it was analyzed, whether they were placed into a pricing category or whether another consumer received a different offer moments earlier.
That invisibility is part of what makes this conversation so significant.
Americans have long accepted certain forms of dynamic pricing. Airline tickets fluctuate with demand, hotels raise prices during major events and ride-share fares increase during busy periods. Traditionally, those systems responded to market conditions affecting consumers collectively. Surveillance pricing raises a different concern because the focus shifts from the market generally to the individual consumer.
Maryland’s legislation reflects that distinction. The law defines dynamic pricing as varying prices within a business day based on demand or other factors, including AI systems capable of recalibrating prices in near real time. It also broadly defines “surveillance data” to include information collected through sensors, cameras, device tracking, biometric monitoring and other technologies capable of gathering personally identifiable information about consumer behavior, location or characteristics.
At a time when many families are already struggling with rising food costs, housing instability and broader economic pressures, the possibility that algorithms could identify who appears most financially constrained, least likely to shop elsewhere or most willing to absorb higher prices raises profound questions about fairness in the digital economy.
These concerns are more than hypothetical.
Walmart recently faced backlash after discussions surrounding electronic shelf labels sparked fears that grocery prices could eventually fluctuate dynamically in real time. Delta Air Lines similarly faced criticism after public discussions around AI-driven pricing raised concerns that companies may eventually move toward systems capable of identifying the maximum amount individual consumers may be willing to pay.
Both Walmart and Delta later clarified aspects of their policies, but the backlash reflected growing public concern about how behavioral data and AI may shape economic decisions behind the scenes.
Once these systems become embedded throughout everyday commerce, regulating them becomes considerably more difficult.
Maryland has acknowledged that enforcing its new law may require specialized technical expertise many regulatory agencies were never designed for. Regulators may eventually need to investigate machine learning systems, behavioral analytics, predictive algorithms, consumer profiling systems and proprietary software capable of recalibrating prices continuously in near real-time.
That creates a different challenge than many traditional consumer protection laws were originally designed to address. With AI, the decision-making process may now be buried inside automated systems, machine learning models, third-party data brokers and predictive analytics tools operating at a pace consumers cannot access or understand.
These challenges reflect a broader misunderstanding about AI That I describe as “The Miseducation of Technology” — the tendency to view AI as an objective force rather than a product of the economic and institutional systems in which it operates. If the digital economy increasingly rewards behavioral prediction, consumer surveillance and profit maximization, AI systems will optimize for those same priorities. If maximizing profit depends on predicting who can be charged more, AI may become remarkably effective at doing exactly that.
For generations, economically vulnerable Americans have paid the poor tax. AI did not create that reality. It may, however, make it faster, more precise, more difficult to detect and capable of operating at unprecedented scale.
Danielle A. Davis Canty is the senior advisor and director of technology policy at the Joint Center for Political and Economic Studies, host of “The Miseducation of Technology” podcast, and a Public Voices fellow on Technology in the Public Interest with The OpEd Project.
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