English(EN) Diversified and Perceptible Counterfactual Examples Leveraging Expert Knowledge

新的XAI方法利用专家知识生成可感知的反事实示例

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

一篇新研究论文介绍了一种名为DiCEf的方法，它是可解释人工智能（XAI）中用于生成反事实示例（CFEs）的DiCE方法的扩展。这种增强方法通过模糊语言词汇整合专家知识，以确保生成的最小输入修改在语义上是有意义的且用户可感知的。该方法允许个性化的CFE，同时保持成本最小化、稀疏性和多样性，这在真实世界数据集上的实验结果得到了证明。 AI

影响通过为用户生成更易于理解的解释来增强AI模型的可解释性。

排序理由该集群包含一篇在arXiv上发表的研究论文，详细介绍了一种新的可解释人工智能方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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Newsworthiness bucket: Tool; 该集群包含一篇在arXiv上发表的研究论文，详细介绍了一种新的可解释人工智能方法。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.AI TIER_1 English(EN) · Akram Bensalem (IMT Atlantique - INFO), Fahima Djelil (Lab-STICC\_MOTEL, IMT Atlantique - INFO), Marie-Jeanne Lesot (IMT Atlantique - INFO, Lab-STICC, Lab-STICC\_MOTEL), Gr{\'e}gory Smits (IMT Atlantique - INFO, Lab-STICC, Lab-STICC\_MOTEL) · 2026-09-15 04:00

利用专家知识实现多样化且可感知的反事实示例

arXiv:2609.15609v1 Announce Type: new Abstract: CounterFactual Examples (CFEs) are a cornerstone of eXplainable Artificial Intelligence (XAI), offering local, post hoc, and model-agnostic explanations by identifying minimal input modifications that alter a model's prediction. Yet…

本文如何被排名

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利用专家知识实现多样化且可感知的反事实示例

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