  As machine learning (ML) systems get adopted in more critical areas, it has
become increasingly crucial to address the bias that could occur in these
systems. Several fairness pre-processing algorithms are available to alleviate
implicit biases during model training. These algorithms employ different
concepts of fairness, often leading to conflicting strategies with
consequential trade-offs between fairness and accuracy. In this work, we
evaluate three popular fairness pre-processing algorithms and investigate the
potential for combining all algorithms into a more robust pre-processing
ensemble. We report on lessons learned that can help practitioners better
select fairness algorithms for their models.
