  In this paper, we focus on the fairness issues regarding unsupervised outlier
detection. Traditional algorithms, without a specific design for algorithmic
fairness, could implicitly encode and propagate statistical bias in data and
raise societal concerns. To correct such unfairness and deliver a fair set of
potential outlier candidates, we propose Deep Clustering based Fair Outlier
Detection (DCFOD) that learns a good representation for utility maximization
while enforcing the learnable representation to be subgroup-invariant on the
sensitive attribute. Considering the coupled and reciprocal nature between
clustering and outlier detection, we leverage deep clustering to discover the
intrinsic cluster structure and out-of-structure instances. Meanwhile, an
adversarial training erases the sensitive pattern for instances for fairness
adaptation. Technically, we propose an instance-level weighted representation
learning strategy to enhance the joint deep clustering and outlier detection,
where the dynamic weight module re-emphasizes contributions of likely-inliers
while mitigating the negative impact from outliers. Demonstrated by experiments
on eight datasets comparing to 17 outlier detection algorithms, our DCFOD
method consistently achieves superior performance on both the outlier detection
validity and two types of fairness notions in outlier detection.
