Recent work uncovered examples of classifiers for which SHAP scores yield
misleading feature attributions. While such examples might be perceived as
suggesting the inadequacy of Shapley values for explainability, this paper
shows that the source of the identified shortcomings of SHAP scores resides
elsewhere. Concretely, the paper makes the case that the failings of SHAP
scores result from the characteristic functions used in earlier works.
Furthermore, the paper identifies a number of properties that characteristic
functions ought to respect, and proposes several novel characteristic
functions, each exhibiting one or more of the desired properties. More
importantly, some of the characteristic functions proposed in this paper are
guaranteed not to exhibit any of the shortcomings uncovered by earlier work.
The paper also investigates the impact of the new characteristic functions on
the complexity of computing SHAP scores. Finally, the paper proposes
modifications to the tool SHAP to use instead one of our novel characteristic
functions, thereby eliminating some of the limitations reported for SHAP
scores.

本篇论文通过探讨 SHAP 得分中存在的问题以及特征函数的影响，提出了几种新的特征函数来替代现有的函数，以消除 SHAP 得分的一些限制。

关于修正 SHAP 分数

On Correcting SHAP Scores

In many studies, we want to determine the influence of certain features on a
dependent variable. More specifically, we are interested in the strength of the
influence -- i.e., is the feature relevant? -- and, if so, how the feature
influences the dependent variable. Recently, data-driven approaches such as
\emph{random forest regression} have found their way into applications
(Boulesteix et al., 2012). These models allow to directly derive measures of
feature importance, which are a natural indicator of the strength of the
influence. For the relevant features, the correlation or rank correlation
between the feature and the dependent variable has typically been used to
determine the nature of the influence. More recent methods, some of which can
also measure interactions between features, are based on a modeling approach.
In particular, when machine learning models are used, SHAP scores are a recent
and prominent method to determine these trends (Lundberg et al., 2017).
In this paper, we introduce a novel notion of feature importance based on the
well-studied Gram-Schmidt decorrelation method. Furthermore, we propose two
estimators for identifying trends in the data using random forest regression,
the so-called absolute and relative transversal rate. We empirically compare
the properties of our estimators with those of well-established estimators on a
variety of synthetic and real-world datasets.

本文介绍了基于 Gram-Schmidt 去相关方法的特征重要性新概念，并提出了用于使用随机森林回归确定数据趋势的绝对和相对横向速率两种估计器。然后，在各种合成和实际数据集上，将我们的估计器的性质与那些知名估计器进行了实证比较。