Explainability in the field of event detection is a new emerging research area. For practitioners and users alike, explainability is essential to ensuring that models are widely adopted and trusted. Several research efforts have focused on the efficacy and efficiency of event detection. However, a human‐centric explanation approach to existing event detection solutions is still lacking. This paper presents an overview of a conceptual framework for human‐centric semantic‐based explainable event detection with the acronym HUSEED. The framework considered the affordances of XAI and semantics technologies for human‐comprehensible explanations of events to facilitate 5W1H explanations (Who did what, when, where, why, and how). Providing this kind of explanation will lead to trustworthy, unambiguous, and transparent event detection models with a higher possibility of uptake by users in various domains of application. We illustrated the applicability of the proposed framework by using two use cases involving first story detection and fake news detection.

中文翻译：

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以人为中心和基于语义的可解释事件检测的概念框架


事件检测领域的可解释性是一个新兴的研究领域。对于从业者和用户来说，可解释性对于确保模型被广泛采用和信任至关重要。一些研究工作集中在事件检测的有效性和效率上。然而，现有事件检测解决方案仍然缺乏以人为本的解释方法。本文概述了以人为中心的基于语义的可解释事件检测的概念框架，首字母缩略词为 HUSEED。该框架考虑了 XAI 和语义技术对事件的可理解性解释，以促进 5W1H 解释（谁做了什么、何时、何地、为什么和如何）。提供这种解释将导致值得信赖、明确和透明的事件检测模型，并且更有可能被各个应用领域的用户采用。我们通过两个涉及首发检测和假新闻检测的用例来说明所提出的框架的适用性。