Black-box Artificial Intelligence (AI) methods, e.g. deep neural networks, have been widely utilized to build predictive models that can extract complex relationships in a dataset and make predictions for new unseen data records. However, it is difficult to trust decisions made by such methods since their inner working and decision logic is hidden from the user. Explainable Artificial Intelligence (XAI) refers to systems that try to explain how a black-box AI model produces its outcomes. Post-hoc XAI methods approximate the behavior of a black-box by extracting relationships between feature values and the predictions. Perturbation-based and decision set methods are among commonly used post-hoc XAI systems. The former explanators rely on random perturbations of data records to build local or global linear models that explain individual predictions or the whole model. The latter explanators use those feature values that appear more frequently to construct a set of decision rules that produces the same outcomes as the target black-box. However, these two classes of XAI methods have some limitations. Random perturbations do not take into account the distribution of feature values in different subspaces, leading to misleading approximations. Decision sets only pay attention to frequent feature values and miss many important correlations between features and class labels that appear less frequently but accurately represent decision boundaries of the model. In this paper, we address the above challenges by proposing an explanation method named Confident Itemsets Explanation (CIE). We introduce confident itemsets, a set of feature values that are highly correlated to a specific class label. CIE utilizes confident itemsets to discretize the whole decision space of a model to smaller subspaces. Extracting important correlations between the features and the outcomes of the classifier in different subspaces, CIE produces instance-wise and class-wise explanations that accurately approximate the behavior of the target black-box. Conducting a set of experiments on various black-box classifiers, and different tabular and textual data classification tasks, we show that our CIE method performs better than the previous perturbation-based and rule-based explanators in terms of the descriptive accuracy (an improvement of 9.3%) and interpretability (an improvement of 8.8%) of the explanations. Subjective evaluations demonstrate that the users find the explanations of CIE more understandable and interpretable than those of the other comparison methods.

黑盒人工智能（AI）方法（例如深度神经网络）已广泛用于构建预测模型，该模型可以提取数据集中的复杂关系并对新的看不见的数据记录进行预测。然而，难以信任通过这种方法做出的决策，因为它们的内部工作和决策逻辑对用户而言是隐藏的。可解释人工智能（XAI）是指试图解释黑匣子AI模型如何产生其结果的系统。事后XAI方法通过提取特征值与预测之间的关系来近似黑匣子的行为。基于扰动和决策集的方法是常用的事后XAI系统。以前的解释者依靠数据记录的随机扰动来构建局部或全局线性模型，这些模型解释单个预测或整个模型。后面的解释器使用出现频率更高的那些特征值来构建一组决策规则，这些决策规则产生的结果与目标黑匣子相同。但是，这两类XAI方法都有一些局限性。随机扰动未考虑特征值在不同子空间中的分布，从而导致误导性近似。决策集仅关注频繁的特征值，而错过了特征与类标签之间的许多重要关联，这些关联不那么频繁出现，但准确地代表了模型的决策边界。在本文中，我们通过提出一种称为“自信项目集说明（CIE）”的说明方法来应对上述挑战。我们引入了可信的项目集，这是一组与特定类标签高度相关的特征值。CIE利用置信项集将模型的整个决策空间离​​散为较小的子空间。CIE提取不同子空间中分类器的特征和结果之间的重要相关性，从而生成了按实例和按类的解释，可以准确地近似目标黑盒的行为。通过对各种黑盒分类器以及不同的表格和文本数据分类任务进行一组实验，我们证明了CIE方法在描述准确度方面比以前的基于扰动和基于规则的解释器更好。 9。3％）和可解释性（提高了8.8％）的解释。主观评估表明，与其他比较方法相比，用户发现CIE的解释更易于理解和理解。