ProfileXAI: User-Adaptive Explainable AI
This work addresses the need for personalized and trustworthy AI explanations for various users, but it is incremental as it builds on existing explainers and LLM techniques.
The paper tackled the problem of generating user-adaptive explanations in explainable AI by developing ProfileXAI, a framework that combines post-hoc explainers with retrieval-augmented LLMs, resulting in stable token usage (σ ≤ 13%) and positive user ratings (x̄ ≥ 3.7) across different user profiles.
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 $\le 0.30$, $L<0.7$ on Heart Disease); Anchor yields the sparsest, low-token rules; SHAP attains the highest satisfaction ($\bar{x}=4.1$). Profile conditioning stabilizes tokens ($σ\le 13\%$) and maintains positive ratings across profiles ($\bar{x}\ge 3.7$, with domain experts at $3.77$), enabling efficient and trustworthy explanations.