Can Ensembling Preprocessing Algorithms Lead to Better Machine Learning Fairness?

In this work, we evaluate three popular fairness preprocessing algorithms and investigate the potential for combining all algorithms into a more robust preprocessing ensemble. We report on lessons learned that can help practitioners better select fairness algorithms for their models.