Fairness-aware Outlier Ensemble

TL;DR

This paper introduces a fairness-aware ensemble framework for outlier detection that balances detection performance with fairness considerations, addressing ethical concerns in sensitive applications.

Contribution

It proposes a novel post-processing method to improve fairness in outlier ensembles without significantly degrading detection accuracy.

Findings

Effective fairness improvement demonstrated on eight datasets.

Trade-off between AUC and fairness analyzed.

Framework maintains competitive detection performance while enhancing fairness.

Abstract

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.