论文标题
人类人类交流的人类沟通：将无监督的无监督异常检测应用于执行教练
Human-AI communication for human-human communication: Applying interpretable unsupervised anomaly detection to executive coaching
论文作者
论文摘要
在本文中，我们讨论了在构建基于AI的交互式系统中应用无监督的异常检测的潜力，该系统与领域专家合作处理高度上下文情况，即人类人类通信。我们通过为执行教练开发计算支持工具的经验来利用无监督的异常检测方法，这教会了我们提供可解释的结果的重要性，以便专家教练可以考虑结果和背景。这种方法背后的关键思想是为专家教练留出空间来释放其开放式解释，而不是简化社交互动的本质，即通过常规监督算法可以解决的定义明确的问题。此外，我们发现这种方法可以扩展到培养新手教练。通过促使他们解释系统的结果，它可以为教练提供教育机会。尽管该方法的适用性应在其他领域进行验证，但我们认为，利用无监督的异常检测来构建基于AI的交互式系统的想法将揭示人类交流的另一个方向。
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.
