Achieving Counterfactual Fairness for Anomaly Detection
Achieving Counterfactual Fairness for Anomaly Detection
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DOI：
10.48550/arxiv.2303.02318
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Xiao Han;Lu Zhang;Yongkai Wu;Shuhan Yuan
中科院分区：
文献类型：
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作者：
Xiao Han;Lu Zhang;Yongkai Wu;Shuhan Yuan
由于许多异常检测应用都涉及到人类，因此保证异常检测模型的公平性受到了广泛关注。然而，现有的公平异常检测方法主要集中在基于关联的公平性的概念。在这项工作中，我们的目标是反事实公平，这是一个普遍的基于因果关系的公平概念。反事实公平异常检测的目标是确保个体在事实世界中的检测结果与个体属于不同群体的反事实世界中的检测结果相同。为此，我们提出了一个反事实公平的异常检测（CFAD）框架，其中包括两个阶段，反事实数据生成和公平的异常检测。在一个合成数据集和两个真实的数据集上的实验结果表明，CFAD能够有效地检测异常，并保证反事实公平性。
Ensuring fairness in anomaly detection models has received much attention recently as many anomaly detection applications involve human beings. However, existing fair anomaly detection approaches mainly focus on association-based fairness notions. In this work, we target counterfactual fairness, which is a prevalent causation-based fairness notion. The goal of counterfactually fair anomaly detection is to ensure that the detection outcome of an individual in the factual world is the same as that in the counterfactual world where the individual had belonged to a different group. To this end, we propose a counterfactually fair anomaly detection (CFAD) framework which consists of two phases, counterfactual data generation and fair anomaly detection. Experimental results on a synthetic dataset and two real datasets show that CFAD can effectively detect anomalies as well as ensure counterfactual fairness.