The rapid advancement of artificial intelligence (AI) and digital technologies has transformed pricing strategies, enabling firms to implement algorithmic and dynamic pricing models. While these strategies enhance efficiency and profitability by leveraging big data and predictive analytics, they also raise significant ethical concerns. This study explores the fairness of AI-driven pricing, particularly in the context of personalized pricing strategies that adjust prices based on consumer data. Drawing from theoretical frameworks such as price fairness, distributive justice, and trust theory, the study examines consumer reactions to algorithmic pricing and the implications for long-term business-consumer relationships.

Empirical evidence suggests that personalized pricing can lead to perceptions of unfairness, especially when consumers are unaware of price differentiation or feel manipulated. While businesses argue that data-driven pricing enhances market efficiency, critics highlight risks such as privacy violations, algorithmic biases, and economic discrimination. Furthermore, AI-driven pricing strategies may exacerbate social inequalities, particularly when used in essential services such as transportation and healthcare.

This study underscores the need for balancing profit-driven pricing models with ethical considerations to maintain customer trust and social responsibility. As AI continues to shape market dynamics, a responsible approach to algorithmic pricing will be essential in fostering ethical business practices and ensuring long-term sustainability.