用概念激活向量解释高维数据中的不确定性，提升模型可信度。
Conceptualizing Uncertainty: A Concept-based Approach to Explaining Uncertainty
- 通过概念激活向量实现局部与全局不确定性解释
- 能揭示不确定性的来源与分布特征，支持模型优化
- 适合关注模型可解释性与信任度的研究者
机器学习中的不确定性指模型预测的置信程度。尽管已有不确定性量化方法，但在高维设置下对不确定性的解释仍具挑战。现有工作多聚焦于局部特征归因，难以提供全局理解。本文提出基于概念激活向量的方法，解释高维数据分类中的不确定性，生成局部与全局解释。我们进一步利用这些解释来改进模型性能，验证其有效性。
原文摘要 · Abstract (English)
Uncertainty in machine learning refers to the degree of confidence or lack thereof in a model's predictions. While uncertainty quantification methods exist, explanations of uncertainty, especially in high-dimensional settings, remain an open challenge. Existing work focuses on feature attribution approaches which are restricted to local explanations. Understanding uncertainty, its origins, and characteristics on a global scale is crucial for enhancing interpretability and trust in a model's predictions. In this work, we propose to explain the uncertainty in high-dimensional data classification settings by means of concept activation vectors which give rise to local and global explanations of uncertainty. We demonstrate the utility of the generated explanations by leveraging them to refine and improve our model.
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