This article aims to show an approach that will make random forest regression a more fair ML algorithm. We show that our “Fair Forest” retains the benefits of the tree-based approach, while providing both greater accuracy and fairness than other alternatives, for both “group fairness” and “individual fairness.” New measures for fairness which are able to handle multinomial and continues attributes as well as regression problems, as opposed to binary attributes and labels only. Finally, we demonstrate a new, more robust evaluation procedure for algorithms that considers the dataset in its entirety rather than only a specific protected attribute.
https://dl.acm.org/doi/pdf/10.1145/3278721.3278742
This article aims to show an approach that will make random forest regression a more fair ML algorithm. We show that our “Fair Forest” retains the benefits of the tree-based approach, while providing both greater accuracy and fairness than other alternatives, for both “group fairness” and “individual fairness.” New measures for fairness which are able to handle multinomial and continues attributes as well as regression problems, as opposed to binary attributes and labels only. Finally, we demonstrate a new, more robust evaluation procedure for algorithms that considers the dataset in its entirety rather than only a specific protected attribute. https://dl.acm.org/doi/pdf/10.1145/3278721.3278742