ABSTRACT
Algorithmic pricing has quietly become a defining feature of online consumer contracting. Using automated models fed by large-scale personal data, firms now set individualized prices by predicting each consumer’s willingness to pay. These practices are widely condemned as ‘unfair’ – yet fairness is invoked more often as a slogan than as a legal standard, leaving courts and regulators without a workable test for when personalization becomes problematic.
This Article supplies that test. It argues that algorithmic price fairness should not be equated with price uniformity. Neither US contract law nor consumer protection law has ever recognized a general right to identical prices, and differential pricing long predates algorithms. An exclusive focus on equality therefore obscures what is genuinely new-and genuinely troubling. The distinctive challenge of algorithmic pricing is informational: prices are shaped not by what parties reveal in bargaining or by broadly observable market conditions, but by inferences drawn from behavioral surveillance and predictive analytics. The result is a structural inversion of classical price formation. Firms accumulate granular knowledge of consumers’ preferences and vulnerabilities; consumers face opacity, degraded comparability, and no meaningful capacity to contest or reciprocate. Reframing the problem as informational resets the regulatory agenda.
Traditional tools – disclosure mandates, unconscionability doctrine, excessive-pricing tests, price caps – are ill-suited to practices that operate through hidden correlations and individualized baselines. This Article proposes instead an augmented price fairness framework built on three complementary safeguards: (1) meaningful explainability of the key factors and data inputs driving a personalized price; (2) access to usable reference prices, including non-personalized or cohort-based benchmarks; and (3) selective data control-enforceable rights to exclude sensitive categories of personal information from pricing algorithms. Together, these pillars restore epistemic symmetry between firms and consumers, protect autonomy and privacy, and supply courts and regulators with administrable standards where none currently exist.
Grochowski, Mateusz, Algorithmic Price Fairness (January 28, 2026).
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