fairmodels: a Flexible Tool for Bias Detection, Visualization, and Mitigation in Binary Classification Models

Machine learning decision systems are becoming omnipresent in our lives. From dating apps to rating loan seekers, algorithms affect both our well-being and future. Typically, however, these systems are not infallible. Moreover, complex predictive models are eager to learn social biases present in historical data that may increase discrimination. If we want to create models responsibly, we need tools for in-depth validation of models also from potential discrimination. This article introduces an R package fairmodels that helps to validate fairness and eliminate bias in binary classification models quickly and flexibly. The fairmodels package offers a model-agnostic approach to bias detection, visualization, and mitigation. The implemented functions and fairness metrics enable model fairness validation from different perspectives. In addition, the package includes a series of methods for bias mitigation that aim to diminish the discrimination in the model. The package is designed to examine a single model and facilitate comparisons between multiple models.