论文标题
基于莎普利价值的解释的问题作为特征的重要性措施
Problems with Shapley-value-based explanations as feature importance measures
论文作者
论文摘要
具有特征重要性的游戏理论表述已成为“解释”机器学习模型的一种方式。这些方法使用游戏的独特Shapley值在模型的特征和在这些输入元素之间分布影响之间定义了合作游戏。这些方法的理由基于两个支柱：它们理想的数学特性及其对特定解释动机的适用性。我们表明，当将沙普利值用于特征重要性时，就会出现数学问题，并且缓解这些必然会引起进一步的复杂性的解决方案，例如需要因果推理。我们还借鉴了其他文献来争辩说，沙普利价值观不提供适合以人为中心的解释性目标的解释。
Game-theoretic formulations of feature importance have become popular as a way to "explain" machine learning models. These methods define a cooperative game between the features of a model and distribute influence among these input elements using some form of the game's unique Shapley values. Justification for these methods rests on two pillars: their desirable mathematical properties, and their applicability to specific motivations for explanations. We show that mathematical problems arise when Shapley values are used for feature importance and that the solutions to mitigate these necessarily induce further complexity, such as the need for causal reasoning. We also draw on additional literature to argue that Shapley values do not provide explanations which suit human-centric goals of explainability.
