Aided by AI, surveillance pricing puts consumers at the mercy of an algorithm. Two people buying the same product or service may see two completely different prices.
The scene is familiar: you’re catching up with a friend over lunch with both your phones placed on the table. Conversations have already covered a range of topics from the mundane to the meaningful. At one point, you casually mention your fondness for the beloved anime series Doraemon.
Later on, as you scroll through social media, you get hit by a barrage of shopping link ads of the character on a mug, a hoodie, and everything in between. How much do these companies really know about us and what control do we have over it?
The problem is not a simple matter of tightening security in the digital space. The more daunting reality is that we have already given access to these companies willingly—from the data we enter on our social media profiles, the posts we like, the queries we enter on search engines, and the cookies we lazily hit “accept all.”
The Brussels Times alleged in 2023 that Uber charges a higher fare if a user has a lower battery percentage. It reported that two ride requests for the same destination yielded different fares.
All this can be used to build hidden profiles to assess a customer’s willingness to pay for a particular goods or service. Former United States Federal Trade Commission (FTC) Chief Technologist Stephanie T. Nguyen argues that this growing reliance on personal data has become part of what regulators described as the commercial surveillance business model in her recent essay in The New York Times.
According to the FTC, the commercial surveillance business model is the infrastructure of collecting, analyzing, and profiting from information about people through technologies they use that enable near constant monitoring of people’s private lives.
Audacious as it seems, big tech companies have so much personal data from people contained in a continuously evolving technology that, according to Associate Professor Soteris Demetriou of Imperial College London, “They now have the ability to effectively know what you could be interested in before you even do.”
Examples of surveillance pricing
Mostly prevalent in the US, surveillance pricing lets companies use browsing histories, locations, and other personal data to set individual prices. This can be data collected in forms of account registrations, email sign-ups, and online purchases. And they say science fiction can be scary.
In a heavily redacted study released by the FTC in 2024, further methods such as a consumer’s interaction with a particular website or app, albeit implicit, such as mouse movement, app scrolling navigation, and inactivity on video viewing are all digital signals that companies can track.
Take, for example, the recent reports on American low-cost airline JetBlue using surveillance pricing to make flying with the company more expensive. This was sparked by a customer who posted on the social media platform X that a flight they needed to book for a funeral increased by US$230 in a day.
Aided by AI, surveillance pricing puts consumers at the mercy of an algorithm. If two people add a product or service to their shopping carts at the same time, they may see two completely different prices.
This is more commonly experienced when booking flights and hotels. Whenever my wife and I compare flights without actually booking them, even if we’re in the same room, we’d often get considerably different prices masked under vouchers, or some time-bound discount coupons, or an exaggerated “premium loyalty tier” with pluses or platinum in its name.
The Washington Post has drawn flak from the public following a recent class action lawsuit filed against it alleging that it used readers’ browsing and subscription data to charge different customers different subscription prices based on their reading habits.
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In a case filed by one of its users from a resident in Washington, DC, Chelsea Blink alleges that the more loyal a reader became, the more data WaPo could gather to estimate how much that person might tolerate paying at renewal. Rather than rewarding loyalty, its system converted subscribers’ engagement into leverage against them.
In another instance of supposed surveillance pricing, Dernière Heure (The Brussels Times), alleged in 2023 that Uber charges a higher fare if that user has a lower battery percentage. It reported that two ride requests for the same destination yielded different fares. The ride booked from a smartphone with only 12% battery remaining was quoted at €17.56 ($19.16), while the same trip requested from a device with an 84% battery level was priced 6% lower at €16.60 ($18.10).
Imagine how a company aims to profit more from a possibly desperate customer who only wants to go home before their phone battery dies. Of course, this is a claim that Uber vehemently denied…and also alluded to 10 years ago by their former Head of Economic Research Keith Chen when he was interviewed by NPR for its podcast Hidden Brain.
Chen revealed in 2016 that Uber users with low battery “[have] a fear of getting stranded somewhere so they are much more willing to spend more.”
Squeezing the everyday consumer
Even though reports on surveillance pricing primarily emanate from the US, this doesn’t mean people across the world can rest easy. This could be simply a case of stronger reportage from the US and institutions in place to crack down on these cases.
Surveillance pricing may be under a new guise and name; however, the practice itself has been subjected to other investigations under the terminologies of “dynamic pricing” or “price discrimination.”
In 2017, academics and co-authors Stephanie Assad, Robert Clark, Daniel Ershov, and Lei Xu released a study published by the University of Chicago called “Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market,” wherein the use of AI to adopt a form of algorithmic pricing has affected competition and facilitated “tacit-collusion” or how companies independently mirror each other’s price behavior changes simply by observing competitors—without communicating at all.
It found that if all gas stations in the German retail gas market used AI in pricing, the technology was trained over time that a drop in prices would lower profits as all other stations matched, whereas increases in prices drove profits. This resulted in a 38% increase in profit margins for gas companies overall.
As far back as 2008, the London-based insurance company Aviva discontinued its “Pay as you drive” car insurance policy after it was revealed that its system used satellite technology to monitor drivers’ travel patterns and provide discounted premiums to customers who avoided high-risk driving periods.
Harvard professor Shoshana Zuboff said in her book, The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power (2019), that this new economic order will claim human experiences as free raw material for hidden commercial practices of extraction, prediction, and sales.
How do we fight this? For starters, exercise your right to privacy as much as possible. Take advantage of privacy browsing (incognito), opting out of unnecessary loyalty programs, clearing cookies, or use a virtual private network (VPN). Or better yet, demand transparency from companies that harvest your data and call for disclosure on how they use personal information in peddling their goods and services to you.
Easier said than done, of course. I am reminded of the speech of Richard Hendricks from the show Silicon Valley (2014) when he called out major tech CEOs on the show before a US Congressional Hearing on data privacy. “They track our every move. They monitor every moment in our lives. And they exploit our data for profit. And you can ask them all the questions you want. But they’re not going to change.”
The way we win, as Hendricks declaimed, is by creating a new, democratic, decentralized Internet built by the people for the people.
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