mlsanityData to 14 Oct 2026

Deep Clustering based Fair Outlier Detection

arXiv 2106.05127 ↗First posted 9 Jun 2021cs.LG cs.CYcited by 19

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Abstract (arXiv)

In this paper, we focus on the fairness issues regarding unsupervised outlier detection. Traditional algorithms, without a specific design for algorithmic fairness, could implicitly encode and propagate statistical bias in data and raise societal concerns. To correct such unfairness and deliver a fair set of potential outlier candidates, we propose Deep Clustering based Fair Outlier Detection (DCFOD) that learns a good representation for utility maximization while enforcing the learnable representation to be subgroup-invariant on the sensitive attribute. Considering the coupled and reciprocal nature between clustering and outlier detection, we leverage deep clustering to discover the intrinsic cluster structure and out-of-structure instances. Meanwhile, an adversarial training erases the sensitive pattern for instances for fairness adaptation. Technically, we propose an instance-level weighted representation learning strategy to enhance the joint deep clustering and outlier detection, where the dynamic weight module re-emphasizes contributions of likely-inliers while mitigating the negative impact from outliers. Demonstrated by experiments on eight datasets comparing to 17 outlier detection algorithms, our DCFOD method consistently achieves superior performance on both the outlier detection validity and two types of fairness notions in outlier detection.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 26 of its 57 references that are in the map. First posted on arXiv 9 Jun 2021.

Builds on

26 of 57 references.

References · 26

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2013Auto-Encoding Variational Bayescited by 4,053
  3. 2017Adversarial Discriminative Domain Adaptationcited by 1,311
  4. 2015Unsupervised Deep Embedding for Clustering Analysiscited by 767
  5. 2014Certifying and removing disparate impactcited by 715
  6. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 586
  7. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  8. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 384
  9. 2018Fairness Without Demographics in Repeated Loss Minimizationcited by 364
  10. 2018Learning Adversarially Fair and Transferable Representationscited by 353
  11. 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 222
  12. 2015Censoring Representations with an Adversarycited by 192
14 more
  1. 2018Path-Specific Counterfactual Fairnesscited by 181
  2. 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
  3. 2020COPOD: Copula-Based Outlier Detectioncited by 146
  4. 2018The Frontiers of Fairness in Machine Learningcited by 141
  5. 2018Efficient GAN-Based Anomaly Detectioncited by 122
  6. 2018An Empirical Study of Rich Subgroup Fairness for Machine Learningcited by 81
  7. 2018Bias Mitigation Post-processing for Individual and Group Fairnesscited by 72
  8. 2018Anomaly Detection with Generative Adversarial Networks for Multivariate Time Seriescited by 55
  9. 2019Unsupervised Representation Learning by Predicting Random Distancescited by 35
  10. 2020FairOD: Fairness-aware Outlier Detectioncited by 24
  11. 2018Clustering with Outlier Removalcited by 10
  12. 2020Fair Outlier Detectioncited by 10
  13. 2017Outlier Detection for Text Data : An Extended Versioncited by 8
  14. 2020Mining Label Distribution Drift in Unsupervised Domain Adaptationcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 4; 2024: 6; 2025: 2; 2026: 1.

19 papers.

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  1. 2022ADBench: Anomaly Detection Benchmarkcited by 232
  2. 2022Achieving Fairness at No Utility Cost via Data Reweighing with Influencecited by 38
  3. 2024Supervised Algorithmic Fairness in Distribution Shifts: A Surveycited by 24
  4. 2022Robust Fair Clustering: A Novel Fairness Attack and Defense Frameworkcited by 13
  5. 2023Fairness in Visual Clustering: A Novel Transformer Clustering Approachcited by 10
  6. 2024Fair Anomaly Detection For Imbalanced Groupscited by 10
  7. 2022Learning Antidote Data to Individual Unfairnesscited by 8
  8. 2023Achieving Counterfactual Fairness for Anomaly Detectioncited by 7
  9. 2021Efficient Algorithms For Fair Clustering with a New Fairness Notioncited by 5
  10. 2022Trustworthy Anomaly Detection: A Surveycited by 5
  11. 2024Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglementcited by 3
  12. 2025Clustering via Self-Supervised Diffusioncited by 3
7 more
  1. 2024Outlier Detection Bias Busted: Understanding Sources of Algorithmic Bias through Data-centric Factorscited by 2
  2. 2024Towards a Unified Framework of Clustering-based Anomaly Detectioncited by 1
  3. 2023Unleashing the Potential of Unsupervised Deep Outlier Detection through Automated Training Stoppingcited by 0
  4. 2023On the Cause of Unfairness: A Training Sample Perspectivecited by 0
  5. 2024Foundations for Unfairness in Anomaly Detection -- Case Studies in Facial Imaging Datacited by 0
  6. 2025ShuffleGate: Scalable Feature Optimization for Recommender Systems via Batch-wise Sensitivity Learningcited by 0
  7. 2026LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systemscited by 0