Machine learning techniques, such as deep learning and ensemble methods, are
widely used in various domains due to their ability to handle complex
real-world tasks. However, their black-box nature has raised multiple concerns
about the fairness, trustworthiness, and transparency of computer-assisted
decision-making. This has led to the emergence of local post-hoc explainability
methods, which offer explanations for individual decisions made by black-box
algorithms. Among these methods, Kernel SHAP is widely used due to its
model-agnostic nature and its well-founded theoretical framework. Despite these
strengths, Kernel SHAP suffers from high instability: different executions of
the method with the same inputs can lead to significantly different
explanations, which diminishes the utility of post-hoc explainability. The
contribution of this paper is two-fold. On the one hand, we show that Kernel
SHAP's instability is caused by its stochastic neighbor selection procedure,
which we adapt to achieve full stability without compromising explanation
fidelity. On the other hand, we show that by restricting the neighbors
generation to perturbations of size 1 -- which we call the coalitions of Layer
1 -- we obtain a novel feature-attribution method that is fully stable,
efficient to compute, and still meaningful.

这篇论文研究了机器学习技术中局部后解释性方法的不稳定性问题，提出了通过改进确定邻居选取的方法以解决不稳定性问题，同时通过限制邻居生成的扰动范围得到了一种全新的特征归因方法。

透过逐层邻居选择来增强稳定性的 SHAP 形态塑造

Shaping Up SHAP: Enhancing Stability through Layer-Wise Neighbor  Selection

A basic task in explainable AI (XAI) is to identify the most important
features behind a prediction made by a black box function $f$. The insertion
and deletion tests of Petsiuk et al. (2018) are used to judge the quality of
algorithms that rank pixels from most to least important for a classification.
Motivated by regression problems we establish a formula for their area under
the curve (AUC) criteria in terms of certain main effects and interactions in
an anchored decomposition of $f$. We find an expression for the expected value
of the AUC under a random ordering of inputs to $f$ and propose an alternative
area above a straight line for the regression setting. We use this criterion to
compare feature importances computed by integrated gradients (IG) to those
computed by Kernel SHAP (KS) as well as LIME, DeepLIFT, vanilla gradient and
input$\times$gradient methods. KS has the best overall performance in two
datasets we consider but it is very expensive to compute. We find that IG is
nearly as good as KS while being much faster. Our comparison problems include
some binary inputs that pose a challenge to IG because it must use values
between the possible variable levels and so we consider ways to handle binary
variables in IG. We show that sorting variables by their Shapley value does not
necessarily give the optimal ordering for an insertion-deletion test. It will
however do that for monotone functions of additive models, such as logistic
regression.

介绍了一种基于归类算法的重点特征提取方法，使用该方法比较了不同特征提取算法在回归问题中的表现，并且基于 Shapley 值考虑了二进制变量的处理方法。