Title: Mitigating Intersectional Bias in Machine Learning: A Novel Approach to Fairness in Automated Decision-Making.

Abstract The growing dependence on machine learning (ML) algorithms for automated decision-making in essential industries including healthcare, banking, and criminal justice has highlighted substantial concerns about algorithmic bias. Despite extensive research aimed at recognizing and alleviating bias, current solutions frequently concentrate on individual social categories such as race or gender neglecting the intricacies of intersectionality. This research examines the existence of intersectional bias, wherein individuals possess multiple marginalized identities, and its consequences for equity in machine learning (ML) models. This paper demonstrates that traditional machine learning methods perpetuate compounding prejudices against intersectional groups through a thorough analysis of the COMPAS dataset and the use of fairness measures, including demographic parity, equal opportunity, and disproportionate effect. A new algorithmic intervention is suggested to reduce these biases, utilizing adversarial techniques to improve fairness across many demographic characteristics. The findings indicate that the suggested method markedly diminishes intersectional bias, providing a more just framework for machine learning applications in critical decision-making contexts. This research enhances the conversation on fairness in machine learning by highlighting the significance of intersectionality and offers practical guidance for practitioners aiming to develop more equitable automated systems. KEYWORDS Machine Learning Algorithmic Bias Intersectionality Fairness in AI Automated Decision-Making Adversarial Debiasing