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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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51#
發(fā)表于 2025-3-30 08:50:57 | 只看該作者
Power, Politics, and the Civil Sphereing post-hoc explanations, however, they fail to make the model itself more interpretable. To fill this gap, we introduce the Concept Distillation Module, the first differentiable concept-distillation approach for graph networks. The proposed approach is a layer that can be plugged into any graph ne
52#
發(fā)表于 2025-3-30 15:04:10 | 只看該作者
53#
發(fā)表于 2025-3-30 16:52:25 | 只看該作者
54#
發(fā)表于 2025-3-30 22:15:32 | 只看該作者
55#
發(fā)表于 2025-3-31 02:23:33 | 只看該作者
Hassan Namazi,Mohsen Mosadegh,Mozhgan Hayasiot (in- or out-of-distribution) to the ones the ML system has been trained on may lead to potentially fatal consequences. Operational data compliance with the training data has to be verified by the data analyst, who must also understand, in operation, if the autonomous decision-making is still safe
56#
發(fā)表于 2025-3-31 05:48:09 | 只看該作者
Popular Narratives of the Cochlear Implantnot discriminate against specific groups of people becomes crucial. Reaching this objective requires a multidisciplinary approach that includes domain experts, data scientists, philosophers, and legal experts, to ensure complete accountability for algorithmic decisions. In such a context, Explainabl
57#
發(fā)表于 2025-3-31 11:50:23 | 只看該作者
978-3-031-44069-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
58#
發(fā)表于 2025-3-31 14:06:19 | 只看該作者
Explainable Artificial Intelligence978-3-031-44070-0Series ISSN 1865-0929 Series E-ISSN 1865-0937
59#
發(fā)表于 2025-3-31 20:54:02 | 只看該作者
https://doi.org/10.1007/978-3-031-44070-0artificial intelligence; interpretable machine learning; causal inference & explanations; argumentative
60#
發(fā)表于 2025-4-1 00:03:09 | 只看該作者
Opening the?Black Box: Analyzing Attention Weights and?Hidden States in?Pre-trained Language Models e recent advancements in pre-trained language models based on transformers and their increasing integration into daily life, addressing this issue has become more pressing. In order to achieve an explainable AI model, it is essential to comprehend the procedural steps involved and compare them with
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