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

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XInsight: Revealing Model Insights for?GNNs with?Flow-Based Explanationsly learn the maximum reward sample. We demonstrate XInsight by generating explanations for GNNs trained on two graph classification tasks: classifying mutagenic compounds with the MUTAG dataset and classifying acyclic graphs with a synthetic dataset that we have open-sourced. We show the utility of

花爭(zhēng)吵

What Will Make Misinformation Spread: An XAI Perspective is proposed for predicting misinformation spread by leveraging GraphSAGE with heterogeneous graph convolution. Secondly, we propose an explanation module that uses gradient-based and perturbation-based methods, to identify what makes misinformation spread by explaining the trained prediction module

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Conference proceedings 2023Chapters “Finding Spurious Correlations with Function-Semantic Contrast Analysis” and “Explaining Socio-Demographic and Behavioral Patterns of Vaccination Against the Swine Flu (H1N1) Pandemic” are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com..

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https://doi.org/10.1007/978-3-031-44067-0artificial intelligence; interpretable machine learning; causal inference & explanations; argumentative

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花爭(zhēng)吵

1865-0929 of Vaccination Against the Swine Flu (H1N1) Pandemic” are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com..978-3-031-44066-3978-3-031-44067-0Series ISSN 1865-0929 Series E-ISSN 1865-0937