  The prevalence of machine learning models in various industries has led to
growing demands for model interpretability and for the ability to provide
meaningful recourse to users. For example, patients hoping to improve their
diagnoses or loan applicants seeking to increase their chances of approval.
Counterfactuals can help in this regard by identifying input perturbations that
would result in more desirable prediction outcomes. Meaningful counterfactuals
should be able to achieve the desired outcome, but also be realistic,
actionable, and efficient to compute. Current approaches achieve desired
outcomes with moderate actionability but are severely limited in terms of
realism and latency. To tackle these limitations, we apply Generative
Adversarial Nets (GANs) toward counterfactual search. We also introduce a novel
Residual GAN (RGAN) that helps to improve counterfactual realism and
actionability compared to regular GANs. The proposed CounteRGAN method utilizes
an RGAN and a target classifier to produce counterfactuals capable of providing
meaningful recourse. Evaluations on two popular datasets highlight how the
CounteRGAN is able to overcome the limitations of existing methods, including
latency improvements of >50x to >90,000x, making meaningful recourse available
in real-time and applicable to a wide range of domains.
