International Journal of Science, Engineering and TechnologyAuthors: Arzu, Assistant Professor, Nikhil Kumar, Dhruv Upreti, Durgesh Rajpurohit, Vanshika thakur
Abstract: Artificial Intelligence (AI) systems are increasingly used in high-stakes decision-making domains such as recruitment, healthcare, finance, and education, where fairness and accountability are critical. Although these systems are often perceived as objective, they can inherit and amplify biases present in historical data, leading to discriminatory outcomes for certain demographic groups. This study investigates algorithmic bias in machine learning models, with a primary focus on measuring and mitigating unfairness using the Disparate Impact Ratio (DIR) and Equal Opportunity Difference (EOD).Using the Adult Income dataset, this research evaluates bias in income prediction tasks where gender is treated as a protected attribute. Two machine learning models Logistic Regression and Decision Tree are implemented to analyze both predictive performance and fairness. The study follows a two-stage experimental design: a baseline model trained on original data, and a bias-mitigated model using the Disparate Impact Remover, a preprocessing technique that adjusts feature distributions to reduce dependency on sensitive attributes.Results demonstrate that bias mitigation techniques can significantly improve fairness metrics, particularly by increasing the Disparate Impact Ratio toward acceptable thresholds and reducing disparities in true positive rates across groups. However, these improvements often come with a trade-off in predictive accuracy, highlighting the inherent tension between fairness and performance. The findings emphasize that no single bias mitigation strategy is universally optimal; instead, effectiveness varies depending on the dataset, model, and application context.This study contributes to the growing field of fairness-aware machine learning by providing a comparative evaluation of bias detection and mitigation techniques in practical settings. It underscores the importance of integrating fairness measures into the AI development lifecycle and offers insights for designing more ethical, transparent, and inclusive AI systems
DOI: http://doi.org/10.5281/zenodo.21735295
