The 'Clever Hans' effect occurs when the learned model produces correct
predictions based on the 'wrong' features. This effect which undermines the
generalization capability of an ML model and goes undetected by standard
validation techniques has been frequently observed for supervised learning
where the training algorithm leverages spurious correlations in the data. The
question whether Clever Hans also occurs in unsupervised learning, and in which
form, has received so far almost no attention. Therefore, this paper will
contribute an explainable AI (XAI) procedure that can highlight the relevant
features used by popular anomaly detection models of different type. Our
analysis reveals that the Clever Hans effect is widespread in anomaly detection
and occurs in many (unexpected) forms. Interestingly, the observed Clever Hans
effects are in this case not so much due to the data, but due to the anomaly
detection models themselves whose structure makes them unable to detect the
truly relevant features, even though vast amounts of data points are available.
Overall, our work contributes a warning against an unrestrained use of existing
anomaly detection models in practical applications, but it also points at a
possible way out of the Clever Hans dilemma, specifically, by allowing multiple
anomaly models to mutually cancel their individual structural weaknesses to
jointly produce a better and more trustworthy anomaly detector.

本文为异常检测模型提供了一种可解释人工智能（XAI）的方法，以突出不同类型模型所使用的相关特征，并揭示了 “Clever Hans” 效应在许多意外形式中的普遍存在，这是由于模型自身的结构使其无法检测到真正相关的特征，提供了警告，并指出通过允许多个异常模型相互取消其各自的结构缺陷来共同产生更好、更可信的异常检测器的可能出路。