The Role of Explainable AI in Bias Mitigation for Hyper-personalization

Abstract

Hyper-personalization involves leveraging advanced data analytics and machine learning models to deliver highly personalized recommendations and consumer experiences. While these methods provide substantial user experience benefits, they raise ethical and technical concerns, notably the risk of propagating or escalating biases. As personalization algorithms become increasingly intricate and complex, biases may inadvertently shape the hyper-personalized content consumers receive, potentially reinforcing stereotypes, thereby limiting exposure to diverse information, and entrenching social inequalities. Explainable AI (XAI) has emerged as a critical approach to enhance transparency, trust, and accountability in complex data models. By making the inner workings and decision-making processes of machine learning models more interpretable, XAI enables stakeholders—starting from developers to policy regulators and end-users—to detect and mitigate biases. This paper provides a comprehensive literature-driven exploration of how XAI methods can assist in bias identification, audits, and mitigation in hyper-personalized systems. We examine state-of-the-art explainability techniques, discuss their applicability, strengths and limitations, and highlight related fairness frameworks, and propose a conceptual roadmap for integrating XAI into hyper-personalized pipelines. We conclude with a discussion on future research directions and the need for interdisciplinary efforts to ensure crafting ethical and inclusive hyper-personalization strategies.