Mitigating Algorithmic Bias and Ensuring Fairness in Machine Learning

This research investigates algorithmic bias in machine learning models and proposes a bias mitigation framework using re-weighting techniques. The study applies fairness evaluation metrics like the Disparate Impact Ratio (DIR) on the UCI Adult dataset and demonstrates improvements in fairness while maintaining model accuracy.