Fairness-aware Outlier Ensemble

Understand

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.