The Ethics of Personalized Pricing: Its Costs, Benefits, and Future

Shortlisted in the 2026 John Locke Institute Essay Competition (Economics).

I.  Introduction

With the advent of AI, humans are able to build systems that not just follow a set of instructions but learn—even without constant supervision. In many sectors, such unsupervised learning is key for developing intelligent systems that can revolutionize everyday processes and find patterns within data that are not easily observable, a capacity that systems trained with supervised learning lack.

These types of models can be applied to almost any field, from disease diagnosis to financial markets modeling, self-driving cars, and notably, pricing algorithms. However, an algorithm to determine prices may seem inefficient: is it not merely the intersection of supply and demand that determines price? This question reveals the depth and complexity of factors that go into price-setting. Pricing algorithms are used by online retailers, airlines, and even grocery stores for setting what they deem to be the optimal price for a product. The theoretical goal of such pricing algorithms, or personalized pricing, is to engage in perfect price discrimination, i.e. charging a customer the exact maximum amount they are willing to pay for a product. No consumer surplus, or potential profits would go unextracted, and firms would maximize their profit. Yet, is such extraction of profit ethical? Is personalized pricing exclusively beneficial for producers, consumers, beneficial to both, or harmful to both?

I argue that while personalized pricing may seem to infringe upon customer privacy, the economic benefits for both consumers and producers resulting from greater allocative efficiency will largely outweigh the social costs induced by pricing algorithms, without straying into unethical territory solely due to the inherent inequality involved. However, this potential advantage is reliant upon the condition that such pricing algorithms are properly regulated for both fairness in price-setting and transparency toward customers.

II.  Privacy Concerns

First, the privacy concerns surrounding the usage of customer data for setting individual prices is one of the most significant barriers for pricing algorithms’ large-scale implementation. A variety of customer metrics, including mouse speed, unpurchased items in a shopping cart, browser inactivity, and more, are frequently stored and used for future individualized price-setting ("FTC Surveillance Pricing Study"). Companies can thereby assess how much a customer desires a product and, furthermore, how much they are willing to pay. On the surface, being able to track such information would lead to information asymmetry in favor of companies that can exploit such information. 

These concerns are not baseless. In a 2025 study, Shiller determined that companies tend to avoid overtly implementing personalized pricing due to negative publicity and customers heading elsewhere. Amazon in particular was found to offer users different prices for products based on their cookies in 2000, and consequently faced backlash (Shiller 608). Due to negative public reactions, firms are incentivized to obfuscate their pricing algorithms and prevent customers from becoming aware that their data is thusly tracked. This new incentive only exacerbates the privacy problem, as firms may not only use customer data for pricing but will also economically benefit from hiding their doing so. 

However, these legitimate concerns are not the end-all be-all for customer safety. The information asymmetry caused by personalized pricing is far from immutable, as customers who are aware of differing prices can use a variety of low-barrier methods to find the best price available to them: checking prices across different devices, clearing their cache, not agreeing to provide certain information, or comparing their prices with friends’ (Mohammed). As personalized pricing becomes more widespread and as consumers become more aware of the algorithms used to set their prices, their adaptation to these modern market conditions is straightforward, narrowing the gap caused by information asymmetry. With full transparency for consumers, personalized pricing avoids accusations of deceit and inequality.

Indeed, much of the information asymmetry stems from customers simply not being aware of personalized pricing algorithms. This is mitigated by recent progress in regulations aimed at increasing awareness, with New York recently passing a law that requires online retailers to inform customers if the price they see for a product was set by an algorithm (Sircar). Such information puts power in the hands of customers, who will be aware that they can utilize the methods mentioned prior for finding an optimal price or simply take their business elsewhere. This would further disincentivize businesses from using pricing algorithms unethically or at all, as it would become a trade-off with other metrics they aim to optimize, such as customer loyalty and retention. 

Privacy concerns surrounding personalized pricing are valid, but they are not necessarily an argument against such practices and are more an indication of how they should be implemented. Price differentiation is already common in worldwide markets. For instance, bazaars, which remain widespread in the Middle East and North Africa, follow this exact principle: price discrimination through negotiation is expected. The main difference between such scenarios and algorithms setting prices, however, is the lack of transparency as to what these algorithms may be using customer data for. 2018’s Cambridge Analytica scandal validates this concern. Millions of Facebook users had their data utilized without consent for political profiling. With how much more unrestricted personal information exists on the internet a decade later, the potential for unlawful data usage has increased substantially when consumer protection regulations are not properly established. 

As personalized pricing increases in popularity, customer awareness matters more. With a lack of such awareness among a sizable portion of customers, there may also be a large degree of information asymmetry that firms can exploit to their advantage. However, with more policies set in place to increase transparency with customers and the low-cost options for consumers to find the best prices generated by said algorithms and allocate their business where they deem fit, privacy concerns surrounding personalized pricing are largely mitigated. 

III.  Economy and Philosophy

As personalized pricing becomes widespread, it would benefit both sides of the market, increasing allocative efficiency for producers and pricing more consumers into the market. In economic theory, in a market where some given product has a fixed price, all consumers whose willingness-to-pay falls below the market price for that product are excluded from the market. Even if the highest price a consumer is willing to pay is above the average cost of production per unit for the firm, hence a profitable transaction for the firm to exploit, the customer is still entirely excluded from the market if the retail price is higher than this number. This gap caused by failed transactions becomes deadweight loss, a market inefficiency that negatively impacts both sides of the market. Producers miss the opportunity for additional profit, and consumers who may otherwise be able to acquire their desired product in a different pricing scenario are priced out. 

Personalized pricing removes this market inefficiency. When a firm charges each customer, in theory, the exact price they are willing to pay, as long as it is still above the firm’s average cost of production for each unit of the product, firms can profit from supplying such customers. Meanwhile, customers also achieve their desired price, potentially lower than in a fixed-price market. Consequently, the market expands with more completed transactions and lower deadweight loss, raising consumer welfare. 

This approach, which actually benefits the least well-off, would likely align with seminal philosophers of society and inequality like John Rawls, who evaluated methods of social engineering and planning based on whether they benefit the least-advantaged. Rawls allowed for societal and economic inequalities, considering individuals’ natural differences and capacities, but his difference principle contends that such inequalities are only just if they intentionally provide the greatest benefit possible to those who are the most underprivileged. Similarly, his contemporary, libertarian Robert Nozick, would look askance at personalized pricing if it were government-enforced and lacked transparency, but would similarly argue that inequalities can exist and be just, though his reasoning would come from the incompatibility of telling people what to do with their money with liberty.

IV. Economic Effects

Offering luxury options or add-ons to products is considered another method for accommodating customers that are willing to pay different amounts. However, such options only provide a few discrete price points rather than perfectly matching the willingness-to-pay of a consumer. Moreover, luxury add-ons still do not solve the problem of customers being priced out of fixed-price markets, making this option inferior to personalized pricing. 

Potential economic gains resulting from personalized pricing are not solely theoretical. In a study of the welfare implications of personalized pricing in a ridesharing platform, personalized pricing was found to increase overall surplus by 5.2%. These personalized pricing algorithms were based on drivers’ waiting times, not consumer data, allowing the platform and drivers to benefit from the different pricing structure while not exploiting user data (Buchholz et al.). The greater allocative efficiency would ultimately benefit customers too, revealing personalized pricing as working well in practice when it employs transparency and avoids tracking and exploiting consumers’ data. 

The effects of personalized pricing algorithms have been found to vary depending on the types of product markets they are applied to. If, for any given product, market competition is high and production costs are low, personalized pricing generally harms firms and benefits consumers. Alternatively, when competition is low or production costs are high, firms benefit and consumers are harmed (Rhodes and Zhou). As more firms quote personalized prices to consumers, competition increases and firms must be more aggressive with prices. Such a scenario introduces a prisoner’s dilemma, in which each firm wants to personalize prices unilaterally, but as all firms take part in competitive markets, total profits may fall and consumers will benefit (Cardillo).

Personalized pricing’s economic impacts are largely positive for consumers, increasing the span of the market and pricing in more market participants. For producers, the economic outcome is moderately positive, as firms can reap greater profits in uncompetitive or high production-cost markets while also, in certain cases, being able to do so without extracting significant customer data.

V. Conclusion

Widely implementing the novel strategy of personalized pricing would cause a major economic shift across markets, prompting varying reactions from producers and consumers. Privacy concerns, while valid, will be largely mitigated by increasing consumer awareness, proper regulation, and easily-accessible countermeasures for customers to use. Economically, consumers are positioned to benefit across most high-volume markets and a greater portion will be priced into previously fixed-price markets, increasing allocative efficiency and reducing deadweight loss. Producers are positioned to benefit as well from widespread personalized pricing when incentivized, though negative publicity and high competition can reduce total profit. Considering that even the contemporary philosophers best-known for calling for justice and freedom within inequality—Rawls and Nozick—would acquiesce that personalized pricing is not an inherently destabilizing force, it is likely that it is here to stay.

Works Cited

Buchholz, Nicholas et al. "Personalized Pricing and the Value of Time: Evidence from Auctioned Cab Rides." Econometrica: Journal of the Econometric Society, vol. 93, May 2025, pp.929 - 958. Wiley Online Library, https://doi.org/10.3982/ECTA18838 

Cardillo, Julian. "Buyer Beware: Does AI-Powered Personalized Pricing Actually Help Consumers? Brandeis Economists Weigh in." Brandeis Stories, 12 August 2025, https://www.brandeis.edu/stories/2025/august/shiller-ai-pricing.html 

"FTC Surveillance Pricing Study Indicates Wide Range of Personal Data Used to Set Individualized Customer Prices." FTC, 17 January 2025, https://www.ftc.gov/news-events/news/press-releases/2025/01/ftc-surveillance-pricing-study-indicates-wide-range-personal-data-used-set-individualized-consumer

Mohammed, Rafi. "How Retailers Use Personalized Prices to Test What You're Willing to Pay." Harvard Business Review, 20 October 2017, https://hbr.org/2017/10/how-retailers-use-personalized-prices-to-test-what-youre-willing-to-pay

Rhodes, Andrew, and Jidong Zhou. "Personalized Pricing and Competition." AEA, vol. 114, July 2024, pp. 2141 - 2170. https://doi.org/10.1257/aer.20221524 

Shiller, Benjamin Reed. "Inconspicuous Personalized Pricing." The Journal of Industrial Economics, vol. 73, December 2025, pp. 608 - 619. Wiley Online Library, https://doi.org/10.1111/joie.70006 

Sircar, Anisha. "How New York's First-In-Nation AI Pricing Law Affects Your Wallet." Forbes, 3 December 2025, https://www.forbes.com/sites/anishasircar/2025/12/03/new-yorks-algorithmic-pricing-law-what-it-does-and-why-it-matters/?ctpv=searchpage

Wagener, Trevor. "Personalized Discounts, Public Gains: The Welfare Case for Algorithmic Pricing." Computer and Communications Industry Association, 8 July 2025, https://ccianet.org/articles/personalized-discounts-public-gains-the-welfare-case-for-al gorithmic-pricing/