On Selective, Mutable and Dialogic XAI: a Review of What Users Say about Diferent Types of Interactive Explanations

Explainability (XAI) has matured in recent years to provide more human-centered explanations of AI-based decision systems. While static explanations remain predominant, interactive XAI has gathered momentum to support the human cognitive process of explaining. However, the evidence regarding the benefts of interactive explanations is unclear. In this paper, we map existing fndings by conducting a detailed scoping review of 48 empirical studies in which interactive explanations are evaluated with human users. We also create a classifcation of interactive techniques specifc to XAI and group the resulting categories according to their role in the cognitive process of explanation: "selective", "mutable" or "dialogic". We identify the efects of interactivity on several user-based metrics. We fnd that interactive explanations improve perceived usefulness and performance of the human+AI team but take longer. We highlight conficting results regarding cognitive load and overconfdence. Lastly, we describe underexplored areas including measuring curiosity or learning or perturbing outcomes.