# ProfileXAI: User-Adaptive Explainable AI

arXiv: https://arxiv.org/abs/2510.22998 · HTML: https://mlsanity.com/paper/2510.22998/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Towards Robust Interpretability with Self-Explaining Neural Networks](https://mlsanity.com/paper/1806.07538/) (2018)
- [On quantitative aspects of model interpretability](https://mlsanity.com/paper/2007.07584/) (2020)
- [LLMs for Explainable AI: A Comprehensive Survey](https://mlsanity.com/paper/2504.00125/) (2025)
- [Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning](https://mlsanity.com/paper/2401.13986/) (2024)
- [XAI for All: Can Large Language Models Simplify Explainable AI?](https://mlsanity.com/paper/2401.13110/) (2024)
- [LLMs for XAI: Future Directions for Explaining Explanations](https://mlsanity.com/paper/2405.06064/) (2024)
- [A Prescriptive Learning Analytics Framework: Beyond Predictive Modelling and onto Explainable AI with Prescriptive Analytics and ChatGPT](https://mlsanity.com/paper/2208.14582/) (2022)

## Abstract (arXiv)

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.