  Fairness in machine learning is crucial when individuals are subject to
automated decisions made by models in high-stake domains. Organizations that
employ these models may also need to satisfy regulations that promote
responsible and ethical A.I. While fairness metrics relying on comparing model
error rates across subpopulations have been widely investigated for the
detection and mitigation of bias, fairness in terms of the equalized ability to
achieve recourse for different protected attribute groups has been relatively
unexplored. We present a novel formulation for training neural networks that
considers the distance of data points to the decision boundary such that the
new objective: (1) reduces the average distance to the decision boundary
between two groups for individuals subject to a negative outcome in each group,
i.e. the network is more fair with respect to the ability to obtain recourse,
and (2) increases the average distance of data points to the boundary to
promote adversarial robustness. We demonstrate that training with this loss
yields more fair and robust neural networks with similar accuracies to models
trained without it. Moreover, we qualitatively motivate and empirically show
that reducing recourse disparity across groups also improves fairness measures
that rely on error rates. To the best of our knowledge, this is the first time
that recourse capabilities across groups are considered to train fairer neural
networks, and a relation between error rates based fairness and recourse based
fairness is investigated.
