XAI for All: Can Large Language Models Simplify Explainable AI?
This work addresses the challenge of XAI being too technical for non-experts, making it more accessible to business professionals and academics, though it is incremental as it applies existing LLM technology to a known bottleneck.
The paper tackled the problem of making Explainable AI (XAI) accessible to non-experts by developing 'x-[plAIn]', a custom Large Language Model that generates audience-specific explanations, and results from use-case studies showed it effectively provides easy-to-understand summaries tailored to different groups.
The field of Explainable Artificial Intelligence (XAI) often focuses on users with a strong technical background, making it challenging for non-experts to understand XAI methods. This paper presents "x-[plAIn]", a new approach to make XAI more accessible to a wider audience through a custom Large Language Model (LLM), developed using ChatGPT Builder. Our goal was to design a model that can generate clear, concise summaries of various XAI methods, tailored for different audiences, including business professionals and academics. The key feature of our model is its ability to adapt explanations to match each audience group's knowledge level and interests. Our approach still offers timely insights, facilitating the decision-making process by the end users. Results from our use-case studies show that our model is effective in providing easy-to-understand, audience-specific explanations, regardless of the XAI method used. This adaptability improves the accessibility of XAI, bridging the gap between complex AI technologies and their practical applications. Our findings indicate a promising direction for LLMs in making advanced AI concepts more accessible to a diverse range of users.