  Machine learning based decision making systems are increasingly affecting
humans. An individual can suffer an undesirable outcome under such decision
making systems (e.g. denied credit) irrespective of whether the decision is
fair or accurate. Individual recourse pertains to the problem of providing an
actionable set of changes a person can undertake in order to improve their
outcome. We propose a recourse algorithm that models the underlying data
distribution or manifold. We then provide a mechanism to generate the smallest
set of changes that will improve an individual's outcome. This mechanism can be
easily used to provide recourse for any differentiable machine learning based
decision making system. Further, the resulting algorithm is shown to be
applicable to both supervised classification and causal decision making
systems. Our work attempts to fill gaps in existing fairness literature that
have primarily focused on discovering and/or algorithmically enforcing fairness
constraints on decision making systems. This work also provides an alternative
approach to generating counterfactual explanations.
