  Outlier ensemble methods have shown outstanding performance on the discovery
of instances that are significantly different from the majority of the data.
However, without the awareness of fairness, their applicability in the ethical
scenarios, such as fraud detection and judiciary judgement system, could be
degraded. In this paper, we propose to reduce the bias of the outlier ensemble
results through a fairness-aware ensemble framework. Due to the lack of ground
truth in the outlier detection task, the key challenge is how to mitigate the
degradation in the detection performance with the improvement of fairness. To
address this challenge, we define a distance measure based on the output of
conventional outlier ensemble techniques to estimate the possible cost
associated with detection performance degradation. Meanwhile, we propose a
post-processing framework to tune the original ensemble results through a
stacking process so that we can achieve a trade off between fairness and
detection performance. Detection performance is measured by the area under ROC
curve (AUC) while fairness is measured at both group and individual level.
Experiments on eight public datasets are conducted. Results demonstrate the
effectiveness of the proposed framework in improving fairness of outlier
ensemble results. We also analyze the trade-off between AUC and fairness.
