In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation of outcomes to practitioners. As anomaly detection algorithms are increasingly used in safety-critical domains, providing explanations for the high-stakes decisions made in those domains has become an ethical and regulatory requirement. Therefore, this work provides a comprehensive and structured survey on state-of-the-art explainable anomaly detection techniques. We propose a taxonomy based on the main aspects that characterize each explainable anomaly detection technique, aiming to help practitioners and researchers find the explainable anomaly detection method that best suits their needs.

翻译：过去二十年间，异常检测领域的大多数研究专注于提升检测精度，而很大程度上忽略了相应方法的可解释性，将结果解释工作留给了实践者。随着异常检测算法越来越多地应用于安全关键领域，为此类领域中的高风险决策提供解释已成为道德与监管要求。因此，本文对当前最先进的可解释异常检测技术进行了全面且结构化的综述。我们基于表征每种可解释异常检测技术的主要维度提出了一种分类体系，旨在帮助实践者与研究人员找到最契合其需求的可解释异常检测方法。