提出可衡量解释多样性的新方法，帮助选对解释工具
Axiomatic Explainer Globalness via Optimal Transport
- 用最优传输理论定义解释分布的全局性度量
- 实验证明该度量能有效区分不同解释器的表现
- 适合需要评估解释稳定性和多样性的研究人员
解释方法的评估与比较常面临挑战。面对众多解释器，从业者往往依赖量化指标进行选择。一个关键差异在于解释结果的多样性：是全部相同、完全分散，还是介于两者之间。本文提出一种新的解释复杂度度量——全局性（globalness），基于最优传输理论，用于分析特征归因与特征选择方法在给定数据集上生成解释的分布特性。我们建立了该度量应具备的公理化性质，并证明所提出的沃瑟斯坦全局性（Wasserstein Globalness）满足这些条件。通过图像、表格和合成数据集的实验验证，该度量不仅能实现解释器间的有意义对比，还能提升可解释性方法的选择效率。
原文摘要 · Abstract (English)
Explainability methods are often challenging to evaluate and compare. With a multitude of explainers available, practitioners must often compare and select explainers based on quantitative evaluation metrics. One particular differentiator between explainers is the diversity of explanations for a given dataset; i.e. whether all explanations are identical, unique and uniformly distributed, or somewhere between these two extremes. In this work, we define a complexity measure for explainers, globalness, which enables deeper understanding of the distribution of explanations produced by feature attribution and feature selection methods for a given dataset. We establish the axiomatic properties that any such measure should possess and prove that our proposed measure, Wasserstein Globalness, meets these criteria. We validate the utility of Wasserstein Globalness using image, tabular, and synthetic datasets, empirically showing that it both facilitates meaningful comparison between explainers and improves the selection process for explainability methods.
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