In this chapter, we focus on managing the human-machine interaction involved in the explanation process and the mechanisms and tools that can be used to implement such management. Explanation is treated as a human–machine co-construction process. We essentially take a constructive approach in the hope that it might help potential designers and developers of such systems. We draw on literature from various fields such as philosophical explorations of social action, studies of human dialog, human–computer interaction, computer-supported collaborative work, multiagent systems, and human–robot interaction and collaboration. We cover the conceptual and decisional ingredients that may be relevant for implementing the socially interactive process that an AI-enabled machine needs when asked to explain the output it has produced when addressing a decision task or when it has to produce a behavior that is understandable and acceptable for a human who is co-acting or simply co-present with the machine. We review the methods and approaches for designing the appropriate decision and interaction capabilities for socially explainable AI (sXAI). First, we review planners that are designed specifically to synthesize preferred (from a human perspective) explanations and behaviors. We then turn to control architectures that have to orchestrate the various machine capabilities dealing with situation assessment, human mental state estimation, planning, goal management, and human–machine interaction in order to ensure proper management of the sXAI interaction.