ProfileXAI: User-Adaptive Explainable AI

ProfileXAI is a model- and domain-agnostic framework that couples post-hoc explainers (SHAP, LIME, Anchor) with retrieval - augmented LLMs to produce explanations for different types of users. The system indexes a multimodal knowledge base, selects an explainer per instance via quantitative criteria, and generates grounded narratives with chat-enabled prompting. On Heart Disease and Thyroid Cancer datasets, we evaluate fidelity, robustness, parsimony, token use, and perceived quality. No explainer dominates: LIME achieves the best fidelity-robustness trade-off (Infidelity ≤ 0.30, L<0.7 on Heart Disease); Anchor yields the sparsest, low-token rules; SHAP attains the highest satisfaction (barx=4.1). Profile conditioning stabilizes tokens (σ ≤ 13%) and maintains positive ratings across profiles (barx≥ 3.7, with domain experts at 3.77), enabling efficient and trustworthy explanations.