Counterfactual Fairness for Predictions using Generative Adversarial
  Networks
Yuchen Ma Dennis Frauen Valentyn Melnychuk Stefan Feuerriegel
Fairness in predictions is of direct importance in practice due to legal,
ethical, and societal reasons. It is often achieved through counterfactual
fairness, which ensures that the prediction for an individual is the same as
that in a counterfactual world under a different sensitive attribute. However,
achieving counterfactual fairness is challenging as counterfactuals are
unobservable. In this paper, we develop a novel deep neural network called
Generative Counterfactual Fairness Network (GCFN) for making predictions under
counterfactual fairness. Specifically, we leverage a tailored generative
adversarial network to directly learn the counterfactual distribution of the
descendants of the sensitive attribute, which we then use to enforce fair
predictions through a novel counterfactual mediator regularization. If the
counterfactual distribution is learned sufficiently well, our method is
mathematically guaranteed to ensure the notion of counterfactual fairness.
Thereby, our GCFN addresses key shortcomings of existing baselines that are
based on inferring latent variables, yet which (a) are potentially correlated
with the sensitive attributes and thus lead to bias, and (b) have weak
capability in constructing latent representations and thus low prediction
performance. Across various experiments, our method achieves state-of-the-art
performance. Using a real-world case study from recidivism prediction, we
further demonstrate that our method makes meaningful predictions in practice.