  Anomaly detection has a wide range of real-world applications, such as bank
fraud detection and cyber intrusion detection. In the past decade, a variety of
anomaly detection models have been developed, which lead to big progress
towards accurately detecting various anomalies. Despite the successes, anomaly
detection models still face many limitations. The most significant one is
whether we can trust the detection results from the models. In recent years,
the research community has spent a great effort to design trustworthy machine
learning models, such as developing trustworthy classification models. However,
the attention to anomaly detection tasks is far from sufficient. Considering
that many anomaly detection tasks are life-changing tasks involving human
beings, labeling someone as anomalies or fraudsters should be extremely
cautious. Hence, ensuring the anomaly detection models conducted in a
trustworthy fashion is an essential requirement to deploy the models to conduct
automatic decisions in the real world. In this brief survey, we summarize the
existing efforts and discuss open problems towards trustworthy anomaly
detection from the perspectives of interpretability, fairness, robustness, and
privacy-preservation.
