Consumer Perceptions of Fairness and Trust in AI-Driven Personalized Pricing

Abstract

The rapid proliferation of artificial intelligence (AI) in digital commerce has given rise to personalized pricing, wherein algorithms dynamically adjust prices for individual consumers based on behavioral data, browsing history, geographic location, and purchase patterns. While this practice offers commercial efficiency for firms, it raises fundamental questions about consumer fairness and trust. This research investigates how consumers perceive the fairness of AI-driven personalized pricing and how such perceptions shape their trust in both AI systems and the companies that deploy them. Employing a mixed-methods primary research design, the study collected data from 155 respondents through a structured online survey comprising quantitative Likert-scale instruments alongside open-ended qualitative responses. The theoretical framework integrates Equity Theory, Information Asymmetry Theory, and the Technology Acceptance Model to interpret findings within established scholarly traditions. Key findings reveal that consumer acceptance of personalized pricing is not binary but conditional: the vast majority of respondents expressed willingness to accept such practices only when accompanied by transparency, data privacy guarantees, and opt-out mechanisms. The mean fairness score of 2.98 out of 5 and the mean AI trust score of 2.97 out of 5 both indicate low-to-moderate perceptions, while 86% of respondents agreed that algorithmic disclosure would significantly improve their trust. Regulatory demand was strong, with 4.22 out of 5 mean agreement on the need for government oversight. These findings carry significant implications for digital marketing strategy, AI ethics, and public policy formulation.