The Role of Explainable AI in Trustworthy Recommender Systems: Systematic Review

Abstract

Abstract Recommender Systems (RSs) are intelligent computational models that provide tailored recommendations to end users, which are extensively deployed across many domains, such as e-commerce websites, social media networks, online job search platforms, Artificial Intelligence (AI) based assistants, healthcare, agriculture, and financial industries. The opaque nature of the underlying complex machine learning models in use raises concerns about the trustworthiness of the RS outcome, particularly in high-stakes decision-making domains such as healthcare and finance. This research addresses a key gap by proposing a hybrid framework for RSs that incorporates complex black-box models to delivercontextual, auditable, and actionable explanations to establish user trust. Hence, this study aims to investigate approaches todesigning a hybrid framework for assessing explainability in RSs, especially when delivering personalized recommendations that involve high-stakes decisions. The review follows a systematic approach to identify 40 most relevant research articles published between 2023 and 2025. Keywords Explainable AI, XAI, Trustworthy Recommender Systems, Explainability

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