  In this paper, we discuss the potential of applying unsupervised anomaly
detection in constructing AI-based interactive systems that deal with highly
contextual situations, i.e., human-human communication, in collaboration with
domain experts. We reached this approach of utilizing unsupervised anomaly
detection through our experience of developing a computational support tool for
executive coaching, which taught us the importance of providing interpretable
results so that expert coaches can take both the results and contexts into
account. The key idea behind this approach is to leave room for expert coaches
to unleash their open-ended interpretations, rather than simplifying the nature
of social interactions to well-defined problems that are tractable by
conventional supervised algorithms. In addition, we found that this approach
can be extended to nurturing novice coaches; by prompting them to interpret the
results from the system, it can provide the coaches with educational
opportunities. Although the applicability of this approach should be validated
in other domains, we believe that the idea of leveraging unsupervised anomaly
detection to construct AI-based interactive systems would shed light on another
direction of human-AI communication.
