  Machine learning decision systems are getting 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 really eager to learn social biases present in historical
data that can lead to increasing discrimination. If we want to create models
responsibly then we need tools for in-depth validation of models also from the
perspective of potential discrimination. This article introduces an R package
fairmodels that helps to validate fairness and eliminate bias in classification
models in an easy and flexible fashion. The fairmodels package offers a
model-agnostic approach to bias detection, visualization and mitigation. The
implemented set of functions and fairness metrics enables model fairness
validation from different perspectives. The package includes a series of
methods for bias mitigation that aim to diminish the discrimination in the
model. The package is designed not only to examine a single model, but also to
facilitate comparisons between multiple models.
