Decoding AI Complexity: SHAP Textual Explanations via LLM for Improved Model Transparency

Abstract

With the continuous advancement of artificial intelligence (AI), particularly in widespread domains such as healthcare and environmental applications, there is an increasing demand for model interpretability. Understanding the decision-making process of models contributes to building trust in them. Hence, the development of Explainable AI (XAI) has become crucial. This study proposes an approach to generate text via a large language model (LLM) for interpretation to enhance the interpretability of SHAP (Shapley Additive exPlanations) plots. The goal is to make the interpretability of model decisions accessible even to non-IT experts through textual explanations.

Topics & Concepts