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Titlebook: Explainable Artificial Intelligence; First World Conferen Luca Longo Conference proceedings 2023 The Editor(s) (if applicable) and The Auth

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发表于 2025-3-21 18:29:11 | 显示全部楼层 |阅读模式
书目名称Explainable Artificial Intelligence
副标题First World Conferen
编辑Luca Longo
视频video
丛书名称Communications in Computer and Information Science
图书封面Titlebook: Explainable Artificial Intelligence; First World Conferen Luca Longo Conference proceedings 2023 The Editor(s) (if applicable) and The Auth
描述This three-volume set constitutes the refereed proceedings of the First World Conference on Explainable Artificial Intelligence, xAI 2023, held in Lisbon, Portugal, in July 2023. .The 94 papers presented were thoroughly reviewed and selected from the 220 qualified submissions. They are organized in the following topical sections: ​.Part I: Interdisciplinary perspectives, approaches and strategies for xAI; Model-agnostic explanations, methods and techniques for xAI, Causality and Explainable AI; Explainable AI in Finance, cybersecurity, health-care and biomedicine..Part II: Surveys, benchmarks, visual representations and applications for xAI; xAI for decision-making and human-AI collaboration, for Machine Learning on Graphs with Ontologies and Graph Neural Networks; Actionable eXplainable AI, Semantics and explainability, and Explanations for Advice-Giving Systems..Part III: xAI for time series and Natural Language Processing; Human-centered explanations and xAI for Trustworthy and Responsible AI; Explainable and Interpretable AI with Argumentation, Representational Learning and concept extraction for xAI..
出版日期Conference proceedings 2023
关键词artificial intelligence; interpretable machine learning; causal inference & explanations; argumentative
版次1
doihttps://doi.org/10.1007/978-3-031-44070-0
isbn_softcover978-3-031-44069-4
isbn_ebook978-3-031-44070-0Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Evaluating Self-attention Interpretability Through Human-Grounded Experimental Protocolntly better than a random baseline regarding average participant reaction time and accuracy. Moreover, data analysis highlights that high probability prediction induces great explanation relevance. This work shows how self-attention can be aggregated and used to explain Transformer classifiers. The
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Causal-Based Spatio-Temporal Graph Neural Networks for Industrial Internet of Things Multivariate Tidata features effectively. Experimental results on industrial datasets demonstrate that the proposed method outperforms existing baselines and achieves state-of-the-art performance. The proposed approach offers a promising solution for accurate and interpretable spatio-temporal data forecasting.
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Development of a Human-Centred Psychometric Test for the Evaluation of Explanations Produced by XAI ability. The questionnaire development process was divided into two phases. First, a pilot study was designed and carried out to test the first version of the questionnaire. The results of this study were exploited to create a second, refined version of the questionnaire. The questionnaire was evalu
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