eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning models into a more digestible form. These methods help to communicate how the model works with the aim of making machine learning models more transparent and increasing the trust of end-users into their output. SHapley Additive exPlanations (SHAP) and Local Interpretable Model Agnostic Explanation (LIME) are two widely used XAI methods particularly with tabular data. In this commentary piece, we discuss the way the explainability metrics of these two methods are generated and propose a framework for interpretation of their outputs, highlighting their weaknesses and strengths.

翻译：可解释人工智能（XAI）方法应运而生，旨在将机器学习模型的黑箱转化为更易理解的形式。这些方法有助于阐释模型的工作机制，从而提高机器学习模型的透明度，并增强最终用户对其输出结果的信任。SHapley加法解释（SHAP）和局部可解释模型无关解释（LIME）是两种广泛使用的XAI方法，尤其在处理表格数据时表现出色。在本评论文章中，我们探讨了这两种方法生成可解释性指标的机制，并提出了一个解释其输出结果的框架，同时指出了它们的优缺点。