The possibility of personalized pricing, where two individuals shopping online for the same product receive different prices, is raising significant concerns. This scenario arises when algorithms predict a user’s willingness to pay higher prices. This type of pricing has raised enough concern for Maryland and Connecticut to enact laws limiting certain forms of surveillance pricing. New York and New Jersey have also passed similar legislation pending their governors’ signatures.
The focus of these legislations varies; Maryland and New Jersey center on grocery stores, while Connecticut enforces broader measures on retail transactions. New York’s proposal targets multiple industries. These regulations highlight fears that AI might shift pricing strategies from standard supply and demand to consumer-specific predictions. Instead of determining a product’s intrinsic value, the question becomes what the consumer will pay.
Black Americans, whose households possess substantially less wealth compared to White households, along with others in low-income brackets, find these concerns familiar. Economically disadvantaged communities have long dealt with the ‘poor tax’, paying more over time due to predatory practices and limited access to affordable services. Now, AI could automate these inequities, making them less noticeable and harder to challenge.
AI doesn’t need to know a consumer’s exact income. It can gauge purchasing capabilities and behavioral patterns from purchase history, browsing habits, location data, and more. While individual signals seem minor, collectively, they form detailed consumer profiles predicting behavior. Unlike evident unequal treatment, algorithmic pricing leads to unnoticed disparities where only the final price is visible.
Dynamic pricing, such as fluctuating airline tickets or hotel rates, is widely accepted as market-based. Surveillance pricing, focusing on individual consumers, presents a unique challenge, as shown in Maryland’s legislation. It distinguishes dynamic pricing as varying prices throughout a business day due to demand or AI-adjusted systems, broadly defining ‘surveillance data’ including sensors, cameras, and device tracking that collects identifiable consumer information.
With current economic strains, the notion that algorithms might pinpoint who can handle inflated prices raises serious questions about fairness in today’s digital marketplace. These fears are not speculative. Companies such as Walmart and Delta Air Lines have faced backlash over concerns regarding real-time price fluctuations based on electronic data.
Enforcing new laws like Maryland’s may demand specialized expertise beyond the capabilities of many regulatory bodies. As these AI-driven systems integrate into commerce, regulating them becomes more complex. Regulatory bodies might need to examine machine learning systems, behavioral analytics, predictive algorithms, and proprietary software involved in price setting.
Such emerging challenges highlight a common misunderstanding of AI, which is often seen as impartial. In reality, AI reflects the economic and institutional priorities within which it functions. As the digital economy emphasizes consumer behavior prediction, surveillance, and profit-making, AI systems will optimize those aspects. If profit focuses on identifying individuals willing to pay more, AI becomes skilled at exploiting this.
For many vulnerable Americans, AI may not have initiated the ‘poor tax’, but it can exacerbate it by scaling and automating it. Danielle A. Davis Canty, an expert advisor and director of technology policy at the Joint Center for Political and Economic Studies, presents these insights in her podcast, “The Miseducation of Technology.”
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