Title: Re-Visiting Explainable AI Evaluation Metrics to Identify The Most Informative Features Show Chinese title

Title: 重新审视可解释人工智能的评估指标以识别最具信息量的特征

Abstract: Functionality or proxy-based approach is one of the used approaches to evaluate the quality of explainable artificial intelligence methods. It uses statistical methods, definitions and new developed metrics for the evaluation without human intervention. Among them, Selectivity or RemOve And Retrain (ROAR), and Permutation Importance (PI) are the most commonly used metrics to evaluate the quality of explainable artificial intelligence methods to highlight the most significant features in machine learning models. They state that the model performance should experience a sharp reduction if the most informative feature is removed from the model or permuted. However, the efficiency of both metrics is significantly affected by multicollinearity, number of significant features in the model and the accuracy of the model. This paper shows with empirical examples that both metrics suffer from the aforementioned limitations. Accordingly, we propose expected accuracy interval (EAI), a metric to predict the upper and lower bounds of the the accuracy of the model when ROAR or IP is implemented. The proposed metric found to be very useful especially with collinear features. Show Chinese abstract

Abstract: 基于功能或代理的方法是用于评估可解释人工智能方法质量的一种常用方法。 该方法使用统计学方法、定义以及新开发的度量指标来进行评估，无需人工干预。 其中，选择性（Selectivity）、移除并重训练（ROAR）和置换重要性（PI）是最常用的度量指标，用于评估可解释人工智能方法的质量，以突出机器学习模型中最显著的特征。 它们指出，如果从模型中移除或置换最具信息量的特征，则模型性能应经历急剧下降。 然而，这两种度量指标的效率受到多重共线性、模型中显著特征的数量以及模型准确性的影响。 本文通过实证例子表明，这两种度量指标都存在上述局限性。 因此，我们提出了预期准确度区间（EAI），这是一种预测在实施ROAR或IP时模型准确度上下限的度量指标。 研究发现，该提出的度量指标在处理共线性特征时非常有用。