In this paper, we propose FairML.jl, a Julia package providing a framework for fair classification in machine learning. In this framework, the fair learning process is divided into three stages. Each stage aims to reduce unfairness, such as disparate impact and disparate mistreatment, in the final prediction. For the pre-processing stage, we present a resampling method that addresses unfairness coming from data imbalances. The in-processing phase consists of a classification method. This can be either one coming from the MLJ.jl package, or a user-defined one. For this phase, we incorporate fair ML methods that can handle unfairness to a certain degree through their optimization process. In the post-processing, we discuss the choice of the cutoff value for fair prediction. With simulations, we show the performance of the single phases and their combinations.
