Feature-Driven vs Language-Based AI Online Gambling Addiction Modeling: Exploring Interpretability Through XGBoost and LLM-Based RAG

The rise of online gambling has increased concern around identifying behavioral addiction in digital environments. Current predictive systems offer limited interpretability and justification for individual-level risk assessments as they often operate as black boxes. This study proposes a hybrid framework that combines a traditional machine learning model (XGBoost) with a language-based Retrieval-Augmented Generation (RAG) system to combat these limitations. Using user-level behavioral and demographic data, an XGBoost classifier was trained and SHAP (SHapley Additive exPlanations) was applied to uncover the key features of addiction. These insights were then incorporated into a large language model (LLM)-based RAG pipeline using sentence-transformer embeddings and FAISS vector retrieval to generate individualized text justifications for each user classification. Through label refinement based on SHAP-ranked feature thresholds and targeted model tuning, the system achieved improved generalization and classification stability, resulting in an AUC of 0.87 while preserving clear, human-readable explanations via the RAG pipeline. This approach demonstrates the potential of integrating structured and unstructured AI techniques in addiction research and risk screening to support more accountable and understandable behavioral health interventions.