mlsanityData to 7 Oct 2026

Exacerbating Algorithmic Bias through Fairness Attacks

arXiv 2012.08723 ↗First posted 16 Dec 2020cs.LG cs.AI cs.CRcited by 47

Not on the map. Why?

Abstract (arXiv)

Algorithmic fairness has attracted significant attention in recent years, with many quantitative measures suggested for characterizing the fairness of different machine learning algorithms. Despite this interest, the robustness of those fairness measures with respect to an intentional adversarial attack has not been properly addressed. Indeed, most adversarial machine learning has focused on the impact of malicious attacks on the accuracy of the system, without any regard to the system's fairness. We propose new types of data poisoning attacks where an adversary intentionally targets the fairness of a system. Specifically, we propose two families of attacks that target fairness measures. In the anchoring attack, we skew the decision boundary by placing poisoned points near specific target points to bias the outcome. In the influence attack on fairness, we aim to maximize the covariance between the sensitive attributes and the decision outcome and affect the fairness of the model. We conduct extensive experiments that indicate the effectiveness of our proposed attacks.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Dec 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 12; 2023: 12; 2024: 11; 2025: 4; 2026: 2.

47 papers.

Sort:

By area:

  1. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  2. 2022Fairness in Graph Mining: A Surveycited by 76
  3. 2023Adversarial attacks and defenses in explainable artificial intelligence: A surveycited by 47
  4. 2021Interpretable Data-Based Explanations for Fairness Debuggingcited by 30
  5. 2022Robust Conversational Agents against Imperceptible Toxicity Triggerscited by 23
  6. 2021Poisoning Attacks on Fair Machine Learningcited by 19
  7. 2021Fairness-Aware PAC Learning from Corrupted Datacited by 18
  8. 2024AI Risk Management Should Incorporate Both Safety and Securitycited by 17
  9. 2023Improving Fair Training under Correlation Shiftscited by 14
  10. 2022Robust Fair Clustering: A Novel Fairness Attack and Defense Frameworkcited by 13
  11. 2022Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networkscited by 12
  12. 2022How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shiftscited by 11
35 more
  1. 2023Bias in Pruned Vision Models: In-Depth Analysis and Countermeasurescited by 11
  2. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
  3. 2024Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Modelscited by 10
  4. 2022Towards Multi-Objective Statistically Fair Federated Learningcited by 8
  5. 2022How Biased are Your Features?: Computing Fairness Influence Functions with Global Sensitivity Analysiscited by 7
  6. 2023Deceptive Fairness Attacks on Graphs via Meta Learningcited by 6
  7. 2023SoK: Unintended Interactions among Machine Learning Defenses and Riskscited by 6
  8. 2024Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspectivecited by 6
  9. 2021Machine Learning for Fraud Detection in E-Commerce: A Research Agendacited by 5
  10. 2021Fairness Degrading Adversarial Attacks Against Clustering Algorithmscited by 5
  11. 2022Subverting Fair Image Search with Generative Adversarial Perturbationscited by 5
  12. 2022Breaking Fair Binary Classification with Optimal Flipping Attackscited by 4
  13. 2022Fairness-aware Regression Robust to Adversarial Attackscited by 4
  14. 2023Adversarial Attacks on Fairness of Graph Neural Networkscited by 4
  15. 2023Survey on AI Ethics: A Socio-technical Perspectivecited by 4
  16. 2024A Catalog of Fairness-Aware Practices in Machine Learning Engineeringcited by 4
  17. 2024EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learningcited by 4
  18. 2024BadFair: Backdoored Fairness Attacks with Group-conditioned Triggerscited by 4
  19. 2023Towards Poisoning Fair Representationscited by 3
  20. 2024The Effect of Data Poisoning on Counterfactual Explanationscited by 3
  21. 2022A Survey on Preserving Fairness Guarantees in Changing Environmentscited by 2
  22. 2023To be Robust and to be Fair: Aligning Fairness with Robustnesscited by 2
  23. 2023TrojFair: Trojan Fairness Attackscited by 2
  24. 2024Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injectionscited by 2
  25. 2025Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifierscited by 2
  26. 2024PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learningcited by 1
  27. 2021FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Datacited by 0
  28. 2023Threats, Vulnerabilities, and Controls of Machine Learning Based Systems: A Survey and Taxonomycited by 0
  29. 2023Fair Graph Machine Learning under Adversarial Missingness Processescited by 0
  30. 2024LayerMatch: Do Pseudo-labels Benefit All Layers?cited by 0
  31. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  32. 2025FairSAM: Fair Classification on Corrupted Image Data Through Sharpness-Aware Minimizationcited by 0
  33. 2025Adversarial Bias: Data Poisoning Attacks on Fairnesscited by 0
  34. 2026SoK: Colluding Adversaries in Machine Learning Pipelinescited by 0
  35. 2026Fairness Attacks on Recommender Systemscited by 0