# Exacerbating Algorithmic Bias through Fairness Attacks

arXiv: https://arxiv.org/abs/2012.08723 · HTML: https://mlsanity.com/paper/2012.08723/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 30 of 47)

- [A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability](https://mlsanity.com/paper/2204.08570/) (2022)
- [Fairness in Graph Mining: A Survey](https://mlsanity.com/paper/2204.09888/) (2022)
- [Adversarial attacks and defenses in explainable artificial intelligence: A survey](https://mlsanity.com/paper/2306.06123/) (2023)
- [Interpretable Data-Based Explanations for Fairness Debugging](https://mlsanity.com/paper/2112.09745/) (2021)
- [Robust Conversational Agents against Imperceptible Toxicity Triggers](https://mlsanity.com/paper/2205.02392/) (2022)
- [Poisoning Attacks on Fair Machine Learning](https://mlsanity.com/paper/2110.08932/) (2021)
- [Fairness-Aware PAC Learning from Corrupted Data](https://mlsanity.com/paper/2102.06004/) (2021)
- [AI Risk Management Should Incorporate Both Safety and Security](https://mlsanity.com/paper/2405.19524/) (2024)
- [Improving Fair Training under Correlation Shifts](https://mlsanity.com/paper/2302.02323/) (2023)
- [Robust Fair Clustering: A Novel Fairness Attack and Defense Framework](https://mlsanity.com/paper/2210.01953/) (2022)
- [Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks](https://mlsanity.com/paper/2209.05957/) (2022)
- [How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts](https://mlsanity.com/paper/2207.01168/) (2022)
- [Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures](https://mlsanity.com/paper/2304.12622/) (2023)
- [Safety in Graph Machine Learning: Threats and Safeguards](https://mlsanity.com/paper/2405.11034/) (2024)
- [Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Models](https://mlsanity.com/paper/2405.03869/) (2024)
- [Towards Multi-Objective Statistically Fair Federated Learning](https://mlsanity.com/paper/2201.09917/) (2022)
- [How Biased are Your Features?: Computing Fairness Influence Functions with Global Sensitivity Analysis](https://mlsanity.com/paper/2206.00667/) (2022)
- [Deceptive Fairness Attacks on Graphs via Meta Learning](https://mlsanity.com/paper/2310.15653/) (2023)
- [SoK: Unintended Interactions among Machine Learning Defenses and Risks](https://mlsanity.com/paper/2312.04542/) (2023)
- [Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspective](https://mlsanity.com/paper/2402.18607/) (2024)
- [Machine Learning for Fraud Detection in E-Commerce: A Research Agenda](https://mlsanity.com/paper/2107.01979/) (2021)
- [Fairness Degrading Adversarial Attacks Against Clustering Algorithms](https://mlsanity.com/paper/2110.12020/) (2021)
- [Subverting Fair Image Search with Generative Adversarial Perturbations](https://mlsanity.com/paper/2205.02414/) (2022)
- [Breaking Fair Binary Classification with Optimal Flipping Attacks](https://mlsanity.com/paper/2204.05472/) (2022)
- [Fairness-aware Regression Robust to Adversarial Attacks](https://mlsanity.com/paper/2211.04449/) (2022)
- [Adversarial Attacks on Fairness of Graph Neural Networks](https://mlsanity.com/paper/2310.13822/) (2023)
- [Survey on AI Ethics: A Socio-technical Perspective](https://mlsanity.com/paper/2311.17228/) (2023)
- [A Catalog of Fairness-Aware Practices in Machine Learning Engineering](https://mlsanity.com/paper/2408.16683/) (2024)
- [EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2410.02042/) (2024)
- [BadFair: Backdoored Fairness Attacks with Group-conditioned Triggers](https://mlsanity.com/paper/2410.17492/) (2024)

## 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.