The recent surge of interest in explainability in artificial intelligence (XAI) is propelled by not only technological advancements in machine learning, but also by regulatory initiatives to foster transparency in algorithmic decision making. In this article, we revise the current concept of explainability and identify three limitations: passive explainee, narrow view on the social process, and undifferentiated assessment of understanding. In order to overcome these limitations, we present explanation as a social practice in which explainer and explainee co-construct understanding on the microlevel. We view the co-construction on a microlevel as embedded into a macrolevel, yielding expectations concerning, e.g., social roles or partner models: Typically, the role of the explainer is to provide an explanation and to adapt it to the current level of understanding of the explainee; the explainee, in turn, is expected to provide cues that guide the explainer. Building on explanations being a social practice, we present a conceptual framework that aims to guide future research in XAI. The framework relies on the key concepts of monitoring and scaffolding to capture the development of interaction. We relate our conceptual framework and our new perspective on explaining to transparency and autonomy as objectives considered for XAI.

Conversational spoken dialogue systems that interact with the user rather than merely reading the text can be equipped with hesitations to manage dialogue flow and user attention. Based on a series of empirical studies, we elaborated a hesitation synthesis strategy for dialogue systems, which inserts hesitations of a scalable extent wherever needed in the ongoing utterance. Previously, evaluations of hesitation systems have shown that synthesis quality is affected negatively by hesitations, but that they result in improvements of interaction quality. We argue that due to its conversational nature, hesitation synthesis needs interactive evaluation rather than traditional mean opinion score (MOS)-based questionnaires. To validate this claim, we dually evaluate our system’s speech synthesis component, on the one hand, linked to the dialogue system evaluation, and on the other hand, in a traditional MOS way. We are thus able to analyze and discuss differences that arise due to the evaluation methodology. Our results suggest that MOS scales are not sufficient to assess speech synthesis quality, leading to implications for future research that are discussed in this paper. Furthermore, our results indicate that synthetic hesitations are able to increase task performance and that an elaborated hesitation strategy is necessary to avoid likability issues.

In recent years there has been a substantial debate about the need for increasingly spontaneous, conversational corpora of spoken interaction that are not controlled or task directed. In parallel the need has arisen for the recording of multi-modal corpora which are not restricted to the audio domain alone. With a corpus that would fulfill both needs, it would be possible to investigate the natural coupling, not only in turn-taking and voice, but also in the movement of participants. In the following paper we describe the design and recording of such a corpus and we provide some illustrative examples of how such a corpus might be exploited in the study of dynamic interaction. The D64 corpus is a multimodal corpus recorded over two successive days. Each day resulted in approximately 4 h of recordings. In total five participants took part in the recordings of whom two participants were female and three were male. Seven video cameras were used of which at least one was trained on each participant. The Optitrack motion capture kit was used in order to enrich information. The D64 corpus comprises annotations on conversational involvement, speech activity and pauses as well as information of the average degree of change in the movement of participants.