Algorithmic fairness is typically studied from the perspective of
predictions. Instead, here we investigate fairness from the perspective of
recourse actions suggested to individuals to remedy an unfavourable
classification. We propose two new fairness criteria at the group and
individual level, which -- unlike prior work on equalising the average
group-wise distance from the decision boundary -- explicitly account for causal
relationships between features, thereby capturing downstream effects of
recourse actions performed in the physical world. We explore how our criteria
relate to others, such as counterfactual fairness, and show that fairness of
recourse is complementary to fairness of prediction. We study theoretically and
empirically how to enforce fair causal recourse by altering the classifier and
perform a case study on the Adult dataset. Finally, we discuss whether fairness
violations in the data generating process revealed by our criteria may be
better addressed by societal interventions as opposed to constraints on the
classifier.

本研究从补救行动的角度研究算法公正性，提出两个新的公正性准则，明确考虑特征之间的因果关系，从而捕捉在物理世界中执行的补救行动的下游效应。我们探讨了我们的标准与其他标准，如反事实公正性的关系，并且证明了补救公正性与预测公正性互补。最后，我们讨论了是否可以通过社会干预来更好地解决数据生成过程中的公正性违规问题，而不是限制分类器。