Explainable AI: A Retrieval-Augmented Generation Based Framework for Model Interpretability

Devansh Guttikonda, Deepika Indran, Lakshmi Narayanan, Tanishka Pasarad, Sandesh B. J.

2025

Abstract

The growing reliance on Machine learning and Deep learning models in industries like healthcare, finance and manufacturing presents a major challenge: the lack of transparency and understanding of how these models make decisions. This paper introduces a novel Retrieval-Augmented Generation (RAG) based framework to tackle this issue. By leveraging Large Language Models (LLMs) and domain-specific knowledge bases, the proposed framework offers clear, interactive explanations of model outputs, making these systems more trustworthy and accessible for non-technical users. The framework’s effectiveness is demonstrated across healthcare, finance and manufacturing, offering a scalable and effective solution that can be applied across industries.

Paper Citation

in Harvard Style

Guttikonda D., Indran D., Narayanan L., Pasarad T. and B. J. S. (2025). Explainable AI: A Retrieval-Augmented Generation Based Framework for Model Interpretability. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 948-955. DOI: 10.5220/0013241300003890

in Bibtex Style

@conference{icaart25,
author={Devansh Guttikonda and Deepika Indran and Lakshmi Narayanan and Tanishka Pasarad and Sandesh B. J.},
title={Explainable AI: A Retrieval-Augmented Generation Based Framework for Model Interpretability},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={948-955},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013241300003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Explainable AI: A Retrieval-Augmented Generation Based Framework for Model Interpretability
SN - 978-989-758-737-5
AU - Guttikonda D.
AU - Indran D.
AU - Narayanan L.
AU - Pasarad T.
AU - B. J. S.
PY - 2025
SP - 948
EP - 955
DO - 10.5220/0013241300003890
PB - SciTePress