Algorithmic Commerce and the Trust Paradox: Harmonizing Hyper-Personalization, Dynamic Pricing, and Explainable AI in Digital Marketplaces

Abstract

Abstract: Background: The integration of Artificial Intelligence (AI) into electronic commerce has revolutionized the sector, enabling unprecedented levels of personalization and pricing efficiency. However, the opacity of complex machine learning models—often described as "black boxes"—has precipitated a crisis of consumer trust. As regulatory frameworks like the European Union’s AI Act emerge, the industry faces a critical juncture between algorithmic optimization and ethical transparency.

Methods: This study employs a systematic integrative review and conceptual framework analysis. We synthesized data from recent academic literature, industry reports on AI marketing, and regulatory documents regarding trust and excellence in AI. The analysis focuses on three core pillars: hyper-personalization engines, dynamic pricing algorithms, and Explainable AI (XAI) methodologies.

Results: The findings indicate a dualistic impact of AI. While AI-driven personalization and dynamic pricing significantly enhance revenue and operational efficiency, they simultaneously increase consumer anxiety regarding data privacy and fairness. Specifically, opaque dynamic pricing is frequently perceived as predatory, whereas transparent personalization is viewed as value-added.

Conclusion: We conclude that the sustainability of AI in e-commerce depends on the adoption of Explainable AI (XAI). By shifting from opaque algorithms to transparent, interpretable models, retailers can adhere to emerging regulations and, more importantly, foster deep consumer trust. The future of algorithmic commerce lies not merely in prediction, but in explanation.