Adversarial Bias: Data Poisoning Attacks on Fairness

The increasing reliance on AI and machine learning in various sectors necessitates a focus on fairness, as highlighted by a recent study on adversarial bias. Researchers conducted a theoretical analysis and experiments demonstrating that a simple adversarial poisoning strategy could induce significant unfairness in naive Bayes classifiers. By injecting a small fraction of carefully crafted adversarial data points into training sets, they were able to bias the model's decision-making against protected groups while still preserving general performance. This method outperformed existing techniques in degrading fairness metrics across multiple models and datasets, showcasing its effectiveness. As AI systems become more integrated into real-world applications, understanding and mitigating fairness vulnerabilities is essential to ensure equitable outcomes for all users.