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English(EN) ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI

新框架ConceptSMILE审计AI概念解释的可靠性

研究人员开发了ConceptSMILE,这是一个新的模型无关框架,旨在审计人工智能中基于概念的解释的可靠性。该框架扩展了现有的基于扰动的逻辑,通过测量扰动输入区域后概念响应的变化来评估概念的可靠性。ConceptSMILE通过归因准确性、代理保真度、忠实性、稳定性和一致性等指标来评估解释。对视网膜眼底图像进行的初步评估,比较了MedSAM和视觉语言模型(VLM)导出的概念,表明不同概念和路径的可靠性各不相同。 AI

影响 提供了一种评估AI解释可靠性的新方法,可能提高对AI系统的信任度。

排序理由 该集群包含一篇详细介绍用于审计AI解释的新框架的研究论文。

在 arXiv cs.AI 阅读 →

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新框架ConceptSMILE审计AI概念解释的可靠性

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该集群包含一篇详细介绍用于审计AI解释的新框架的研究论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Mohadeseh Mollapour, Koorosh Aslansefat, Zeinab Dehghani, Bhupesh Kumar Mishra, Tejal Shah, Zhibao Mian ·

    ConceptSMILE:审计基于概念的可解释人工智能的可靠性

    arXiv:2607.09649v1 Announce Type: new Abstract: Concept-based explainable artificial intelligence (AI) can make model reasoning more human-understandable, but concept-level outputs are not automatically trustworthy. We introduce ConceptSMILE, a model-agnostic perturbation-based a…

  2. arXiv cs.AI TIER_1 English(EN) · Zhibao Mian ·

    ConceptSMILE:审计基于概念的可解释人工智能的信任度

    Concept-based explainable artificial intelligence (AI) can make model reasoning more human-understandable, but concept-level outputs are not automatically trustworthy. We introduce ConceptSMILE, a model-agnostic perturbation-based auditing framework for evaluating the reliability…