The interdisciplinary field of explainable artificial intelligence (XAI) aims to foster human understanding of black-box machine learning models through explanation-generating methods. Although the social sciences suggest that explanation is a social and iterative process between an explainer and an explainee, explanation user interfaces and their user interactions have not been systematically explored in XAI research yet. Therefore, we review prior XAI research containing explanation user interfaces for ML-based intelligent systems and describe different concepts of interaction. Further, we present observed design principles for interactive explanation user interfaces. With our work, we inform designers of XAI systems about human-centric ways to tailor their explanation user interfaces to different target audiences and use cases.