Human-AI communication for human-human communication: Applying interpretable unsupervised anomaly detection to executive coaching
Riku Arakawa, Hiromu Yakura

TL;DR
This paper explores using interpretable unsupervised anomaly detection to enhance AI systems that support human-human communication, especially in executive coaching, emphasizing interpretability and open-ended analysis.
Contribution
It introduces a novel approach of applying unsupervised anomaly detection with interpretability to AI-based coaching tools, enabling expert and novice engagement.
Findings
Interpretable anomaly detection aids expert coaches in understanding complex social interactions.
The approach supports educational opportunities for novice coaches.
The method emphasizes open-ended interpretation over predefined problem-solving.
Abstract
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…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Data Visualization and Analytics · Big Data and Business Intelligence
