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.
翻译:异常探测具有广泛的真实世界应用,例如银行欺诈探测和网络入侵探测。过去十年,开发了各种异常探测模型,导致在准确发现各种异常方面取得重大进展。尽管取得了成功,异常探测模型仍面临许多限制。最重要的是,我们能否相信这些模型的检测结果。近年来,研究界作出了巨大努力来设计可靠的机器学习模型,例如开发可靠的分类模型。然而,对异常探测任务的关注远远不够。考虑到许多异常探测任务涉及人类的生命变化任务,将某人标为异常或欺诈者应当极为谨慎。因此,确保以可信方式进行的异常探测模型是部署模型在现实世界中进行自动决定的一个基本要求。在本次简短的调查中,我们从可解释性、公平性、稳健性和隐私保护的角度,总结现有努力,并讨论在可信赖的异常探测方面的公开问题。