A Survey on Explainable Anomaly Detection
It tackles the problem of making anomaly detection more interpretable for users in high-stakes domains, but it is incremental as it surveys existing work rather than introducing new methods.
This survey addresses the lack of explainability in anomaly detection methods, which is crucial for safety-critical applications, by providing a structured taxonomy of state-of-the-art techniques to guide practitioners and researchers.
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.