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。