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.

通过开发一种名为生成对抗平衡网络（GCFN）的新颖深度神经网络，本文研究了在反事实公平性下进行预测的方法，并通过一种新颖的反事实调节器正则化方式，借助量身定制的生成对抗网络直接学习敏感属性后代的反事实分布，较好地保证了反事实公平性的概念。该方法解决了现有基于推断潜在变量的基线方法存在的问题，并在各种实验证明，其性能达到了最先进水平。在自由刑滞后预测的现实案例研究中，我们进一步证明了我们的方法在实践中能够作出有意义的预测。