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A Conceptual Framework for Human-Centric and Semantics-Based Explainable Event Detection
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DOI：10.1002/widm.1565.png)
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Abstract
En 中文
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. A conceptual framework for human-centric and semantic-based explainable event detection (HUSEED).image
Keywords:
event detection
explainable AI
explainable event detection
human-centric explanations
semantic-based explainable AI
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