Fair Data Representation for Machine Learning at the Pareto Frontier

As machine learning powered decision-making becomes increasingly important in our daily lives, it is imperative to strive for fairness in the underlying data processing. We propose a pre-processing algorithm for fair data representation via which supervised learning results in estimations of the Pareto frontier between prediction error and statistical disparity. In particular, the present work applies the optimal affine transport to approach the post-processing Wasserstein barycenter characterization of the optimal fair $L^2$-objective supervised learning via a pre-processing data deformation. Furthermore, …

algorithm data data processing decision error estimations fair fairness machine machine learning making pareto prediction pre-processing processing representation statistical supervised learning