Artificial Intelligence (AI) has a communication problem. XAI methods have been used to make AI more understandable and helped resolve some of the transparency issues that inhibit AI's broader usability. However, user evaluation studies reveal that the often numerical explanations provided by XAI methods have not always been effective for many types of users of AI systems. This article aims to adapt the major communications models from Science Communications into a framework for practitioners to understand, influence, and integrate the context of audiences both for their communications supporting AI literacy in the public and in designing XAI systems that are more adaptive to different users.

翻译：人工智能（AI）存在传播问题。可解释人工智能（XAI）方法已被用于提升AI的可理解性，并有助于解决阻碍AI广泛应用的透明度问题。然而，用户评估研究表明，XAI方法提供的数值型解释并不总是对所有类型的AI系统用户有效。本文旨在将科学传播领域的主要传播模型改编为一个实践框架，帮助从业者理解、影响并整合受众的语境，从而既支持面向公众的AI素养传播，又能设计出对不同用户更具适应性的XAI系统。