Source code to Evaluating Fairness in Machine Learning: Comparative Analysis of Fairlearn and AIF360

The implementation and effectiveness of fairness toolkits in machine learning, specifically AIF360 and Fairlearn, significantly differ in terms of ease of integration, data handling capabilities, metric variety, and algorithmic performance. These differences can substantially impact their adoption and effectiveness in various socio- economic and organizational contexts, influencing the overall fairness of machine learning systems in real-world applications. This work aims to evaluate the differences between those most popular fairness toolkits.