通过睡眠专家提高分组公平性
Advancing subgroup fairness via sleeping experts
摘要：
我们研究的方法，以提高公平性的设置与重叠的人口和顺序预测的子组。经典的公平概念关注的是不同人群之间某些财产的平衡。然而，在许多应用中，不同组的目标不是被同等地预测，而是被很好地预测。我们证明，满足多个重叠组的保证的任务是不简单的，并表明，对于简单的目标的未加权平均的假阴性和假阳性率，满足这重叠的人群可以是统计上不可能的，即使我们提供的预测，以及分别在每个子组。在积极的一面，我们表明，当个人是同样重要的，他们属于不同的群体，这一目标是可以实现的;要做到这一点，我们提请连接到睡眠专家文献在在线学习。出于对自然环境中的单边反馈的兴趣，我们将我们的结果扩展到这样的反馈模型。我们还提供了一个博弈论的解释，我们的结果，研究参与者加入系统的动机，并提供系统的全部信息，他们可能拥有的预测。我们结束了几个有趣的开放性问题的强度保证，可以实现在计算效率的方式。
英文摘要：
We study methods for improving fairness to subgroups in settings with overlapping populations and sequential predictions. Classical notions of fairness focus on the balance of some property across different populations. However, in many applications the goal of the different groups is not to be predicted equally but rather to be predicted well. We demonstrate that the task of satisfying this guarantee for multiple overlapping groups is not straightforward and show that for the simple objective of unweighted average of false negative and false positive rate, satisfying this for overlapping populations can be statistically impossible even when we are provided predictors that perform well separately on each subgroup. On the positive side, we show that when individuals are equally important to the different groups they belong to, this goal is achievable; to do so, we draw a connection to the sleeping experts literature in online learning. Motivated by the one-sided feedback in natural settings of interest, we extend our results to such a feedback model. We also provide a game-theoretic interpretation of our results, examining the incentives of participants to join the system and to provide the system full information about predictors they may possess. We end with several interesting open problems concerning the strength of guarantees that can be achieved in a computationally efficient manner.
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