SHAP评分在多种模型中普遍存在误导性，即使模型满足平滑性要求。
SHAP scores fail pervasively even when Lipschitz succeeds
- 通过布尔分类器和回归模型证明SHAP评分问题普遍存在
- 即便模型满足Lipschitz连续性，SHAP评分仍可能严重失真
- 适用于关注可解释性可信度的研究者与实践者
Shapley值在可解释人工智能（XAI）中广泛应用，常被称为SHAP评分。近期研究揭示了某些机器学习分类器中计算出的SHAP评分存在严重缺陷，可能导致人类决策者被误导。然而，这些例子曾被认为过于人为，因需将类别解释为数值。本文回应这一批评：首先，在布尔分类器中存在任意多例使SHAP评分必然不令人满意；其次，此类问题同样存在于回归模型中。此外，本文研究了满足Lipschitz连续性的回归模型（该性质在模型鲁棒性中具有重要应用），发现即使满足此条件，SHAP评分问题依然存在。最后，证明对于任意可微分回归模型，此类问题也必然存在。
原文摘要 · Abstract (English)
The ubiquitous use of Shapley values in eXplainable AI (XAI) has been triggered by the tool SHAP, and as a result are commonly referred to as SHAP scores. Recent work devised examples of machine learning (ML) classifiers for which the computed SHAP scores are thoroughly unsatisfactory, by allowing human decision-makers to be misled. Nevertheless, such examples could be perceived as somewhat artificial, since the selected classes must be interpreted as numeric. Furthermore, it was unclear how general were the issues identified with SHAP scores. This paper answers these criticisms. First, the paper shows that for Boolean classifiers there are arbitrarily many examples for which the SHAP scores must be deemed unsatisfactory. Second, the paper shows that the issues with SHAP scores are also observed in the case of regression models. In addition, the paper studies the class of regression models that respect Lipschitz continuity, a measure of a function's rate of change that finds important recent uses in ML, including model robustness. Concretely, the paper shows that the issues with SHAP scores occur even for regression models that respect Lipschitz continuity. Finally, the paper shows that the same issues are guaranteed to exist for arbitrarily differentiable regression models.
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