How Are Trends in Explainable AI Redefining E-Commerce? From Opaque to Transparent Decisions Through Semantic Analysis

Research interest in Explainable Artificial Intelligence (XAI) has surged since 2018, accelerating after 2020. Our research explores the role of XAI in e-commerce, emphasizing its ability to enhance transparency, interpretability and trust in AI-driven systems. To address the limited research on XAI in e-commerce, this paper conducts a bibliometric and semantic analysis of 8,845 publications from the Web of Science. It employs four advanced topic modeling techniques: Latent Dirichlet Allocation (LDA), BERT-LDA, BERT-Clustering and BERTopic. The research identifies XAI tools, popular XAI applications in e-commerce and emerging trends. The analysis also examines market type evolution and measures research momentum through the Compound Annual Growth Rate (CAGR). Keywords such as “Natural Language Processing”, “Neural Networks”, “Transformer Models” and “SHAP” exhibit the highest CAGR values, exceeding 80%. Using Hugging Face NER pipeline, we obtain that SHAP is the most frequently used tool with 2,182 mentions, followed by ICE, ALE and CEM. Extracting “market” from abstracts, the analysis reveals a significant shift in XAI research from traditional financial markets to emerging areas like energy, sustainability and environmental sciences. In e-commerce, fraud detection is the most prominent XAI application with nearly 40 mentions, followed by inventory management, price prediction, churn and demand forecasting with 15-20 mentions each. Topic 2 by BERTopic is closely related to e-commerce and focuses on the theme of recommender systems.