  Anomaly detection aims to find instances that are considered unusual and is a
fundamental problem of data science. Recently, deep anomaly detection methods
were shown to achieve superior results particularly in complex data such as
images. Our work focuses on deep one-class classification for anomaly detection
which learns a mapping only from the normal samples. However, the non-linear
transformation performed by deep learning can potentially find patterns
associated with social bias. The challenge with adding fairness to deep anomaly
detection is to ensure both making fair and correct anomaly predictions
simultaneously. In this paper, we propose a new architecture for the fair
anomaly detection approach (Deep Fair SVDD) and train it using an adversarial
network to de-correlate the relationships between the sensitive attributes and
the learned representations. This differs from how fairness is typically added
namely as a regularizer or a constraint. Further, we propose two effective
fairness measures and empirically demonstrate that existing deep anomaly
detection methods are unfair. We show that our proposed approach can remove the
unfairness largely with minimal loss on the anomaly detection performance.
Lastly, we conduct an in-depth analysis to show the strength and limitations of
our proposed model, including parameter analysis, feature visualization, and
run-time analysis.
