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XAI for All: Can Large Language Models Simplify Explainable AI?
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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.
Forward citations
Cited by 4 Pith papers
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Mind the XAI Gap: A Human-Centered LLM Framework for Democratizing Explainable AI
An in-context LLM framework that produces dual expert and non-expert explanations, evaluated on well-being clustering with a user study and LIME-alignment metrics.
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Triadic Fusion of Cognitive, Functional, and Causal Dimensions for Explainable LLMs: The TAXAL Framework
TAXAL proposes a triadic cognitive-functional-causal framework for role-sensitive explainability in agentic LLMs, demonstrated through cross-domain case studies.
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Composable Building Blocks for Controllable and Transparent Interactive AI Systems
A framework for making interactive AI systems transparent by exposing their structural and visual building blocks through a shared API.
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Towards Transparent AI: A Survey on Explainable Large Language Models
A review that groups LLM explainability methods by transformer architecture and discusses their evaluation and applications.
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