Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. Furthermore, it has been shown that XAI can be used to extract input-output relationships, making them a useful tool in chemistry to understand structure-property relationships. However, one of the main limitations of XAI methods is that they are developed for technically oriented users. We propose the XpertAI framework that integrates XAI methods with large language models (LLMs) accessing scientific literature to generate accessible natural language explanations of raw chemical data automatically. We conducted 5 case studies to evaluate the performance of XpertAI. Our results show that XpertAI combines the strengths of LLMs and XAI tools in generating specific, scientific, and interpretable explanations.

翻译：可解释人工智能是人工智能领域的一个新兴方向，旨在解决机器学习模型的黑箱特性。研究表明，可解释人工智能可用于提取输入-输出关系，使其成为化学领域中理解构效关系的有效工具。然而，可解释人工智能方法的主要局限性之一在于其开发目标群体为技术型用户。我们提出XpertAI框架，该框架将可解释人工智能方法与可访问科学文献的大型语言模型相结合，自动生成原始化学数据的可理解自然语言解释。我们通过5个案例研究评估了XpertAI的性能。结果表明，XpertAI融合了大型语言模型与可解释人工智能工具的优势，能够生成具体、科学且可解释的说明。