Abstract
We introduce a causal framework for designing optimal policies that satisfy fairness constraints. We take a pragmatic approach asking what we can do with an action space available to us and only with access to historical data. We propose two different fairness constraints: a moderation breaking constraint which aims at blocking moderation paths from the action and sensitive attribute to the outcome, and by that at reducing disparity in outcome levels as much as the provided action space permits; and an equal benefit constraint which aims at distributing gain from the new and maximized policy equally across sensitive attribute levels, and thus at keeping pre-existing preferential treatment in place or avoiding the introduction of new disparity. We introduce practical methods for implementing the constraints and illustrate their uses on experiments with semi-synthetic models.
Abstract (translated)
我们引入了一个因果框架,用于设计满足公平约束的最优政策。我们采取了实用的方法,问我们可以利用我们所拥有的行动空间和只能访问历史数据的能力,可以做哪些事情。我们提出了两种不同的公平约束:一种 moderation breaking 约束,旨在阻止从行动和敏感属性到结果的 moderation 路径,并尽可能减少提供的行动空间所允许的结果差距;另一种是平等收益约束,旨在将新的收益均匀地分配到敏感属性水平的所有级别上,并最大限度地增加政策的收益,从而保持现有的优先待遇或避免引入新的差距。我们介绍了实施约束的实用方法,并在模拟实验中展示了它们的使用情况。
URL
https://arxiv.org/abs/2301.12278