  The rise in machine learning-assisted decision-making has led to concerns
about the fairness of the decisions and techniques to mitigate problems of
discrimination. If a negative decision is made about an individual (denying a
loan, rejecting an application for housing, and so on) justice dictates that we
be able to ask how we might change circumstances to get a favorable decision
the next time. Moreover, the ability to change circumstances (a better
education, improved credentials) should not be limited to only those with
access to expensive resources. In other words, \emph{recourse} for negative
decisions should be considered a desirable value that can be equalized across
(demographically defined) groups. This paper describes how to build models that
make accurate predictions while still ensuring that the penalties for a
negative outcome do not disadvantage different groups disproportionately. We
measure recourse as the distance of an individual from the decision boundary of
a classifier. We then introduce a regularized objective to minimize the
difference in recourse across groups. We explore linear settings and further
extend recourse to non-linear settings as well as model-agnostic settings where
the exact distance from boundary cannot be calculated. Our results show that we
can successfully decrease the unfairness in recourse while maintaining
classifier performance.
