PUB - Publikationen an der Universität Bielefeld

Emotions play an important role in human decision-making. However, first approaches to incorporating knowledge of this influence into AI-based decision support systems are only very recent. Accordingly, our target is to develop an interactive intelligent agent that is capable of explaining the recommendations of AI-systems while taking emotional constraints into account. This article addresses the following research questions based on the emotions of happiness and anxiety: (1) How do induced emotions influence risk propensity in HCI? (2) To what extent does the explanation strategy influence the human explanation recipient in a lottery choice? (3) How well can an HCI system estimate the emotional state of the human? Our results showed that (1) our emotion induction strategy was successful. However, the trend took the opposite direction of ATF predictions. (2) Our explanation strategy yielded a change in the risk decision in only 26% of the participants; in some cases, participants even changed their selection in the opposite direction. (3) Emotion recognition from facial expressions did not provide sufficient indications of the emotional state - because of head position and a lack of emotional display - but heart rate showed significant effects of emotion induction in the expected direction. Importantly, in individual cases, the dynamics of facial expressions followed the expected path. We concluded that (1) more differentiated explanation strategies are needed, and that temporal dynamics may play an important role in the explanation process, and (2) that a more interactive setting is required to elicit more emotional cues that can be used to adapt the explanation strategy accordingly.