Analysis and Comparison of Different Bias Mitigation Strategies on Machine Learning Models

The task of Bias mitigation allows for more accurate and fair AI sys- tems, as Bias in models can lead to discriminatory outcomes and skewed data analysis. In this project, we explore different kinds of Bias mitiga- tion techniques, and compare their effectiveness on different types Machine Learning models trained on the same dataset. In addition, we will analyze their effect on the model’s performance. Finally, we will experiment with a combination of the methodologies.