PUB - Publikationen an der Universität Bielefeld

To produce explanations that are more likely to be accepted by humans, Explainable Artificial Intelligence (XAI) systems need to incorporate explanation models grounded in human communication patterns. So far, little is known about how an explainee, who lacks understanding of an issue, and an explainer, who has knowledge to fill the explainee's knowledge gap, actively shape an explanation process, and how their involvement relates to explanatory success in terms of maximizing the explainee's level of understanding. In this paper, we characterize explanations as dialogues in which explainee and explainer take turns to advance the explanation process. We build on an existing annotation scheme of ‘explanatory moves’ to characterize such turns, and manually annotate 362 dialogical explanations from the “Explain Like I'm Five” subreddit. Building on the annotated data, we compute correlations between explanatory moves and explanatory success, measured on a five-point Likert scale, in order to identify factors that are significantly correlated with explanatory success. Based on a qualitative analysis of these factors, we develop a conceptual model of the main factors that contribute to the success of explanatory dialogues.