  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.
