Gender Disparities in Predictive Income Modeling: Advancing Fairness through Algorithmic Interventions

Research Article

Abstract

As predictive modeling increasingly influences decision-making across domains, concerns about fairness and bias have gained prominence. One critical area of concern is gender bias in income prediction models. This article explores state-of-the-art bias mitigation techniques, focusing on their application to address gender disparities. Through a combination of pre-processing, in-processing, and post-processing methods, this research demonstrates how fairness can be integrated into predictive modeling frameworks without compromising accuracy. Additionally, this study examines the trade-offs between fairness and accuracy, providing insights into balancing ethical considerations with technical performance. A novel contribution is the development of hybrid mitigation strategies that combine multiple techniques to maximize effectiveness. Real-world datasets are used to validate the approaches, highlighting practical challenges and opportunities in mitigating bias. Furthermore, this research explores the implications of fairness-aware modeling on policy design and its potential to foster inclusive decision-making processes. The findings contribute to a growing body of knowledge aimed at ensuring equitable outcomes in machine learning applications while offering actionable guidance for practitioners and policymakers alike.