Securing Machine Learning: Understanding Adversarial Attacks and Bias Mitigation

Abstract

This paper offers a comprehensive examination of adversarial vulnerabilities in machine learning (ML) models and strategies for mitigating fairness and bias issues. It analyses various adversarial attack vectors encompassing evasion, poisoning, model inversion, exploratory probes, and model stealing, elucidating their potential to compromise model integrity and induce misclassification or information leakage. In response, a range of defence mechanisms including adversarial training, certified defences, feature transformations, and ensemble methods are scrutinized, assessing their effectiveness and limitations in fortifying ML models against adversarial threats. Furthermore, the study explores the nuanced landscape of fairness and bias in ML, addressing societal biases, stereotypes reinforcement, and unfair treatment, proposing mitigation strategies like fairness metrics, bias auditing, de-biasing techniques, and human-in-the-loop approaches to foster fairness, transparency, and ethical AI deployment. This synthesis advocates for interdisciplinary collaboration to build resilient, fair, and trustworthy AI systems amidst the evolving technological paradigm.