Differentiating prices has always shown more than just the basic math of business. It is about what people are thought to be worth, not just how much something costs. The story seemed almost comical when ProPublica revealed that Orbitz displayed more costly hotel options to Mac users. Beneath the humor, however, was a remarkably similar echo of past practices that punished groups for who they were rather than what they purchased.
Dynamic pricing is frequently praised as being especially inventive because of how well it matches supply and demand. The speed at which prices change in response to surges in demand is best illustrated by airlines and ride-sharing services. However, research published in the Harvard Business Review showed that algorithms inherit the hidden biases present in data and do more than just optimize. Even when race was removed from the model, historical practices that charged minority communities higher mortgage rates were covertly absorbed and repeated. Bias discovered a brand-new mask that was remarkably resilient.
| Issue | Details |
|---|---|
| Core Question | How does charging different prices uncover deeper inequality beyond profit motives? |
| Historic Examples | Segregated restaurant counters, redlining in mortgages, racially biased school meal practices. |
| Modern Mechanisms | Algorithmic pricing, targeted advertising, psychological pricing strategies. |
| Notable Cases | Orbitz showing higher-priced hotels to Mac users; Princeton Review charging more in Asian-majority ZIP codes. |
| Impact on Society | Reinforces disparities, erodes consumer trust, and magnifies systemic inequality. |
This conundrum was encapsulated in the Princeton Review case. Parents found that online SAT preparation courses were much more expensive in Asian-majority neighborhoods than in other areas. Families were asked to pay up to $8,400 for the same course in Queens, whereas it cost $6,600 elsewhere. Merit and demand alone were insufficient to explain the disparity; it served as a reminder that history, geography, and identity all interact to produce injustices that algorithms can unapologetically magnify.
These differences are further exploited by psychological pricing, which takes advantage of the manipulation of human value perception. Although it may seem harmless, charm pricing—setting an item at $9.99 rather than $10—has proven to be incredibly successful in influencing consumer behavior. According to research on left-digit bias in financial markets, even experienced investors exhibit irrational reactions when a number shifts from 10.00 to 9.99. When businesses believe we can or will pay more, they use the same deceptive tactics that give us the impression that a deal is within our grasp to defend premiums.
The painfully obvious manifestation of this is seen in celebrity culture. Fans watched in real time as Beyoncé’s tour tickets skyrocketed in price as algorithmic demand and loyalty clashed. In addition to exposing inefficiencies, Taylor Swift’s Eras Tour disaster, in which ticket systems failed under duress, also brought to light how pricing policies marginalize those without privilege, leaving devoted fans outside the gates while scalpers took advantage of automated flaws. In this case, pricing exposed the brittleness of entertainment justice rather than merely reflecting demand.
The more profound reality is that charging disparate prices damages confidence. According to Deloitte research, three-quarters of customers would stop doing business with a company if they discovered that its systems discriminated against particular groups. Reputations harmed by discriminatory pricing rarely fully recover, and once trust is lost, it is rarely restored quickly. For this reason, fairness is a very effective survival strategy for brands rather than a luxury.
Efforts to regulate demonstrate the increasing urgency. While the US is still more lenient, Europe already prohibits location-based price discrimination. The European Union’s AI Act treats pricing algorithms as high-risk systems, and the Federal Trade Commission has started to increase its scrutiny. These actions imply that governments have finally realized that unfair pricing is not only immoral but also detrimental to society. Although it is rarely quick enough, the law is gradually catching up with technology.
One cannot overlook the links to systemic racism. Digital systems that use proxy data, such as ZIP codes or online activity, have replicated historical redlining practices that prevented Black families from obtaining affordable mortgages. Algorithms can reproduce race indirectly even when it is not present. Because discrimination is concealed under the neutral language of efficiency, it is more difficult to identify and more readily denied, which leads to especially harmful results.
Whole communities bear the social cost. Families lose purchasing power, children miss out on opportunities, and wealth disparities deepen when groceries become more expensive in particular neighborhoods or when loan rates rise due to invisible proxies. In The Sum of Us, Heather McGhee makes a very strong case that racism and inequality harm not just the disadvantaged but also the entire economy, which lowers prosperity for all. A system devalues itself when it excludes, even in subtle ways.
However, advancement is still feasible. Businesses can test algorithms for disparate impacts by implementing fairness toolkits, such as IBM’s open-source AI Fairness 360. The responsible AI guidelines from Microsoft demonstrate how diverse teams can identify blind spots before they become systemic problems. Even a small amount of transparency can have unexpected benefits. Giving consumers a general but truthful explanation of the factors that influence prices reassures them that the figures are not capricious penalties but rather well-considered choices that are subject to examination.
Setting different prices makes us face the many guises of inequality, which reveals more than bias. It demonstrates how history is reflected in data, how justice can be compromised for immediate benefit, and how dignity itself can be valued. However, it also reveals opportunities: the ability to restore confidence, create extraordinarily successful systems that put equity and efficiency first, and change a culture of secrecy to one of openness.
