Your browsing history could soon set your grocery bill — and Canada isn't ready for it

Summary

"Your browsing history could soon set your grocery bill — and Canada isn't ready for it" Jake Okechukwu Effoduh, The Conversation (May 3, 2026)

The article examines surveillance pricing (also called algorithmic personalized pricing) — a practice that uses personal data to estimate how much consumers are willing to pay and adjusts the price accordingly. It argues that Canadian law is not equipped to regulate this emerging practice, leaving consumers exposed to price discrimination they cannot detect.

What is surveillance pricing?

Surveillance pricing differs from dynamic pricing (used by airlines, hotels, and rideshare companies). Dynamic pricing adjusts based on market conditions like demand or time of day and applies the same algorithm to every customer equally. Surveillance pricing, by contrast, draws on browsing history, device, postal code, purchase frequency, and inferred income to predict a person's willingness to pay — charging different customers different prices for the same item at the same moment.

The Canadian context

An NDP motion to ban surveillance pricing was voted down in April 2026 by the Liberals and Conservatives. A poll found that when the practice was explained, 52% of Canadians said it should be banned and another 31% said it should be more strictly regulated.

The most prominent documented case came from the U.S.: an investigation found Instacart customers were charged up to 23% more than other shoppers for the same items at the same store. Instacart halted the program in December 2025 following public backlash. Canadian retailers are assembling the same toolkit: digital shelf labels, AI-driven pricing engines, and loyalty card data.

Where Canadian law falls short

The article identifies three legal regimes that circle the problem but none that covers it:

• Competition law — The Competition Bureau's mandate is to protect competition, not consumer fairness. Its tools catch anti-competitive behaviour between companies, not price discrimination between individual shoppers.
• Consumer protection law — Provincial acts like Ontario's Consumer Protection Act are designed for misleading practices in one-on-one transactions, not large-scale automated price differences across millions of consumers.
• Privacy law — Governs consent to data collection, but not consent to how that data is used to shape what you pay.

What other jurisdictions have done

• New York — The Algorithmic Pricing Disclosure Act (November 2025) requires businesses using personalized pricing to display a notice, with civil penalties of up to US$1,000 per violation.
• European Union — Has required disclosure of personalized pricing since its 2019 consumer rights overhaul.
• Manitoba — Bill 49, introduced by Premier Wab Kinew's NDP government in March 2026, would go further and prohibit surveillance pricing outright.

Ontario Premier Doug Ford said he would not follow Manitoba's lead, telling reporters he believes in a "free market."

The good news

The article notes that shoppers can use private browsing, turn off location services, and log out of loyalty apps as short-term workarounds. However, these place the burden on consumers and require a level of digital awareness not everyone has. Real protection would require either a federal disclosure mandate (along New York's lines) or an outright prohibition (like Manitoba's Bill 49). The Competition Bureau can keep monitoring, but monitoring is not enforcement.

Vault connections

• Capitalism and Schizophrenia — Algorithmic surveillance pricing operates as an empirical realization of Deleuze and Guattari's "capitalist axiomatic" and the "dividual": standardized price codes are dissolved into real-time, quantitative modulations where each consumer is atomized into data vectors to extract maximum surplus value.
• Shoshana Zuboff-The Age of Surveillance Capitalism — Surveillance pricing represents a direct commercial manifestation of Zuboff's "behavioral surplus" and "prediction products," showing how behavioral telemetry is annexed to compute individual willingness to pay, eroding consumer sanctuary and entrenching epistemic inequality.
• The Attention Economy — Surveillance pricing is a direct application of attention-economy logic: extracting behavioural data and monetizing it through personalized price discrimination. The article's concern about opaque algorithmic pricing mirrors the Attention Economy's critique of extractive architectures.
• Governing Algorithmic Risk - Public Policy Frameworks for AI Accountability — The article's call for disclosure mandates and algorithmic accountability directly engages with this note's framework for regulating AI-driven systems, including algorithmic impact assessments and risk-proportionate oversight.
• Why Canada Will Never Have a WeChat — Examines how Canada's regulatory framework (PIPEDA, Competition Bureau, privacy commissioners) shapes digital markets, providing context for why Canadian law is structurally unprepared for surveillance pricing.
• Net Neutrality — Both notes examine how regulatory architecture struggles to keep pace with digital market practices. Net neutrality's concern with ISP gatekeeper power parallels surveillance pricing's concern with retailer gatekeeping over prices.
• Consumers — The article's analysis of how surveillance pricing exploits consumer behaviour connects to this note's foundational discussion of who consumers are and how their behaviour is studied and shaped.
• Pricing Strategy — Surveillance pricing is a radical extension of the value-based pricing model explored in this note, where willingness-to-pay estimation moves from market research to real-time individual profiling.
• Planned Obsolescence — Both notes examine extractive corporate architectures that prioritize revenue over consumer welfare. The opacity of surveillance pricing mirrors the engineered information asymmetry of planned obsolescence.