By John Q. Hosedrinker 
New Democratic Party Leader Avi Lewis has called on the federal government to ban algorithmic pricing, a practice where retailers use AI and data to set different prices for consumers. Lewis described this “surveillance pricing” as “downright creepy” and a “rip-off,” alleging that Big Tech and retailers are collaborating to exploit Canadians. The NDP plans to introduce a parliamentary motion to prohibit this dynamic pricing, a move echoed by the United Food and Commercial Workers Union. Recent polling indicates that a majority of Canadians believe algorithmic pricing is unfair and should be banned or more strictly regulated, with concerns also raised by the Competition Bureau and consumer advocacy groups regarding its potential impact on pricing.
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The push for a ban on algorithmic pricing, championed by the NDP and directed at figures like Carney, highlights a growing unease about how prices are determined in our increasingly digital world. The core of the concern, often described as “downright creepy,” lies in the idea that prices are no longer static, determined by traditional factors of supply and demand and perhaps some room for negotiation. Instead, they are now fluid, shifting based on algorithms that can analyze vast amounts of data about consumers, their habits, and even their perceived immediate needs.
This shift to dynamic pricing, especially within physical retail spaces, is seen by many as a deeply concerning development. While price fluctuations have always existed in certain sectors, like gas or market goods, the idea of digital price tags changing in real-time as a shopper walks by is particularly unsettling. It feels like a departure from the predictability consumers have come to expect, a subtle form of price gouging where the consumer is always at a disadvantage due to an inherent information asymmetry.
The problem isn’t simply that prices change; it’s how they change and *why*. The concern is that algorithms are being used to identify individual price sensitivity and then charge each person the absolute maximum they might be willing to pay. This is essentially personalized price discrimination, a scenario where companies could price fix an individual without them even realizing it. The thought of this extending to physical stores, where digital tags could adjust based on a consumer’s online search history or even their proximity within the store, paints a picture of an intrusive and predatory marketplace.
This creeping surveillance is a major point of contention. The idea that a store might read a phone’s IMEI or Bluetooth ID to gauge a customer’s presence and shopping habits, and then adjust prices accordingly, sounds like science fiction but is perceived by many as a plausible, even inevitable, next step given current data collection practices. The infrastructure for this kind of tracking, from sensors in stores to the ubiquitous nature of online data harvesting, is already in place, making the transition to algorithmic pricing a financially attractive, albeit ethically dubious, move for corporations.
The contrast between this sophisticated, data-driven pricing and the traditional market is stark. In the past, haggling or seeking out different vendors provided some recourse for consumers. Today, with dominant players like Amazon or limited domestic markets in sectors like groceries, consumers have fewer alternatives. When prices can shift hourly online or even minute-by-minute in a physical store based on algorithms, the consumer is left with little power to negotiate or even predict what they will pay. This creates a situation where a consumer might be faced with a price surge for an essential item simply because the algorithm detected a localized need.
Furthermore, the argument that dynamic pricing inherently benefits consumers by offering lower prices during off-peak times is met with skepticism. While some instances of time-based or yield pricing might have valid reasons and should be openly labeled, the current trajectory of algorithmic pricing is perceived as exploitative. It is not about offering discounts for planning ahead but about extracting maximum profit through subtle, individualized price hikes based on a wealth of personal data. This is seen as a departure from the fundamental purpose of markets, which should ideally be to improve human lives, not just optimize for financial gain at the expense of human well-being.
The concern extends beyond consumer prices to wages, with the analogy of dynamic wages for dynamic pricing being raised. If companies can adjust prices based on perceived demand and individual ability to pay, why wouldn’t wages fluctuate similarly, potentially leading to situations where overtime pay could be astronomically high for some, while others are exploited based on their financial circumstances, as seen in contract nursing. This exemplifies a broader trend of “enshittification,” where platforms and markets are designed to extract maximum value for corporations and investors, often at the detriment of both consumers and workers.
Ultimately, the call to ban algorithmic pricing stems from a deep-seated distrust of unchecked corporate power and a desire for transparency and fairness in the marketplace. The sentiment is that while markets can be efficient, their efficiency should serve human needs, not exploit them. The current direction of algorithmic pricing, characterized by its opacity and potential for individual manipulation, is seen as a step in a dystopian direction, undermining fundamental principles of fair trade and consumer protection. The NDP’s intervention, urging figures like Carney to address this issue, signifies a growing public demand for regulatory action to curb these practices and ensure that technology serves humanity rather than exploiting it.