The R Package fairmodelsfacilitates** bias detection through model visualizations.** It implements a few mitigation strategies that could reduce bias. It enables easy to use checks for fairness metrics and comparison between different Machine Learning (ML) models.
Bias mitigation is an important topic in Machine Learning (ML) fairness field. For python users, there are algorithms already implemented, well-explained, and described (see AIF360). fairmodels provides an implementation of a few popular, effective bias mitigation techniques ready to make your model fairer.
Having a biased model is not the end of the world. There are lots of ways to deal with it. **fairmodels **implements various algorithms to help you tackle that problem. Firstly, I must describe the difference between the pre-processing algorithm and the post-processing one.
In this section, I will briefly describe how these bias mitigation techniques work. Code for more detailed examples and some visualizations used here may be found in this vignette.
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