Explainable Artificial Intelligence in Consumer-Centric Business Practices and Approaches

Abstract

The review begins by discussing the fundamental concepts of XAI, highlighting its significance in enhancing consumer trust and engagement in AI-driven services and products. The authors explore various dimensions of XAI, such as interpretability, transparency, and accountability, and their implications in consumer-centric contexts. This study identifies a range of methods and techniques adopted by businesses to implement XAI, including rule-based systems, model-agnostic approaches, and interpretable machine learning models. Content analysis is employed as the primary research method in this review. The findings of this SLR provide a holistic overview of the current state of XAI in consumer-centric business practices and approaches, helping businesses and researchers gain a better understanding of the field's evolution and the challenges that lie ahead. It also underscores the importance of ethical considerations and regulatory frameworks in fostering responsible AI adoption within consumer-focused domains.