Explainable Artificial Intelligence (XAI) provides tools to help understanding how the machine learning models work and reach a specific outcome. It helps to increase the interpretability of models and makes the models more trustworthy and transparent. In this context, many XAI methods were proposed being SHAP and LIME the most popular. However, the proposed methods assume that used predictors in the machine learning models are independent which in general is not necessarily true. Such assumption casts shadows on the robustness of the XAI outcomes such as the list of informative predictors. Here, we propose a simple, yet useful proxy that modifies the outcome of any XAI feature ranking method allowing to account for the dependency among the predictors. The proposed approach has the advantage of being model-agnostic as well as simple to calculate the impact of each predictor in the model in presence of collinearity.

翻译：可解释人工智能（XAI）提供了帮助理解机器学习模型如何工作并得出特定结果的工具。它有助于增强模型的可解释性，使模型更加可信和透明。在此背景下，人们提出了许多XAI方法，其中SHAP和LIME最为流行。然而，这些方法假设机器学习模型中的预测变量是相互独立的，但实际情况通常并非如此。这一假设给XAI结果的稳健性（例如信息性预测变量的列表）蒙上了阴影。本文提出了一种简单而有效的代理方法，该方法可对任何XAI特征排序方法的输出进行修正，从而考虑预测变量之间的依赖关系。该方法具有模型无关的优势，并且易于计算存在共线性时每个预测变量对模型的影响。