ECHO: Enhancing Conversational Explainable AI through Tool-Augmented Language Models

Abstract

This paper introduces ECHO, an LLM-powered system framework to explore and interrogate the internals of AI models through tool-augmented language models. While traditional XAI methods typically offer a small and technical set of explanation types, ECHO advances the accessibility and usability of AI explanations through a conversational approach, combining LLMs with a collection of tools and a human-in-the-loop process. We identify various explanation types from the literature, for which we create a set of predefined tools for tabular data. Using a modular architecture, ECHO integrates these predefined tools with dynamically generated tools to interact with AI models, facilitating tailored explanations for a large variety of user queries. This paper details ECHO’s design, implementation, and use cases, demonstrating its capabilities in the context of a movie recommender, healthcare decision tree and neural network for educational classification.