Machine learning (ML) is increasingly often used to inform high-stakes decisions. As complex ML models (e.g., deep neural networks) are often considered black boxes, a wealth of procedures has been developed to shed light on their inner workings and the ways in which their predictions come about, defining the field of 'explainable AI' (XAI). Saliency methods rank input features according to some measure of 'importance'. Such methods are difficult to validate since a formal definition of feature importance is, thus far, lacking. It has been demonstrated that some saliency methods can highlight features that have no statistical association with the prediction target (suppressor variables). To avoid misinterpretations due to such behavior, we propose the actual presence of such an association as a necessary condition and objective preliminary definition for feature importance. We carefully crafted a ground-truth dataset in which all statistical dependencies are well-defined and linear, serving as a benchmark to study the problem of suppressor variables. We evaluate common explanation methods including LRP, DTD, PatternNet, PatternAttribution, LIME, Anchors, SHAP, and permutation-based methods with respect to our objective definition. We show that most of these methods are unable to distinguish important features from suppressors in this setting.

翻译：机器学习（ML）越来越多地被用于影响高风险决策。由于复杂的ML模型（如深度神经网络）常被视为黑箱，人们开发了大量程序来揭示其内部运作及预测产生的方式，从而定义了“可解释人工智能”（XAI）领域。显著性方法根据某种“重要性”度量对输入特征进行排序。由于目前缺乏特征重要性的正式定义，此类方法难以验证。已有研究表明，部分显著性方法可能突出与预测目标无统计关联的特征（抑制变量）。为避免因这类行为导致的误解，我们提出将这种关联的实际存在作为特征重要性的必要条件及客观初步定义。我们精心构建了一个所有统计依赖关系明确且呈线性的地面实况数据集，作为研究抑制变量问题的基准。我们基于客观定义评估了包括LRP、DTD、PatternNet、PatternAttribution、LIME、锚点、SHAP及基于排列的方法在内的常见解释方法。结果表明，在此设定下，大多数方法无法区分重要特征与抑制变量。