Summary of Fair Anomaly Detection For Imbalanced Groups, by Ziwei Wu and Lecheng Zheng and Yuancheng Yu and Ruizhong Qiu and John Birge and Jingrui He
Fair Anomaly Detection For Imbalanced Groups
by Ziwei Wu, Lecheng Zheng, Yuancheng Yu, Ruizhong Qiu, John Birge, Jingrui He
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper introduces a novel approach to anomaly detection in imbalanced scenarios, ensuring model fairness for both protected and unprotected groups. The existing methods concentrate on the dominating unprotected group, leading to incorrect labeling of normal examples from the protected group as anomalies. To address this issue, FairAD is presented, comprising a fairness-aware contrastive learning module and a rebalancing autoencoder module. These modules are designed to ensure fairness while handling imbalanced data. Theoretical analysis shows that the proposed contrastive learning regularization guarantees group fairness, while empirical studies demonstrate the effectiveness and efficiency of FairAD across multiple real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to detect anomalies in situations where one group is much larger than another. This is important because many current methods only look at the bigger group and miss anomalies from the smaller group. The researchers suggest using two modules: one that helps make sure the model is fair, and another that balances the data so it’s not too imbalanced. They show that their method works well on real-world datasets. |
Keywords
» Artificial intelligence » Anomaly detection » Autoencoder » Regularization
