Personalized Pricing and Data Discrimination

The development of the digital economy and the extensive use of personal data have facilitated the emergence of sophisticated commercial practices, among which personalised pricing occupies a central position. Through the use of algorithms, profiling techniques, and artificial intelligence, companies can adjust their commercial offers according to the individual characteristics and behaviour of consumers, raising concerns regarding fairness, transparency, and economic discrimination. The aim of this paper is to analyse personalised pricing as a contemporary form of price discrimination and to highlight the role of personal data and algorithms in differentiating the economic treatment of consumers. The research methodology consists of a theoretical and documentary analysis based on a review of relevant economic and interdisciplinary literature, as well as an examination of the legal and ethical implications of using data in price-setting processes. The findings indicate that personalised pricing can generate efficiency gains and enable a better alignment of commercial offers with demand characteristics; however, under conditions of limited transparency and information asymmetry, it may lead to subtle forms of economic and indirect discrimination. Consequently, algorithmic transparency, responsible data use, and stronger consumer protection mechanisms are essential conditions for limiting the potentially discriminatory effects of personalised pricing in the digital economy.