Popularizing Fairness: Group Fairness and Individual Welfare
Popularizing Fairness: Group Fairness and Individual Welfare
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DOI：
10.1609/aaai.v37i6.25910
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Andrew Estornell;Sanmay Das;Brendan Juba;Yevgeniy Vorobeychik
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文献类型：
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作者：
Andrew Estornell;Sanmay Das;Brendan Juba;Yevgeniy Vorobeychik
群体公平的学习方法通常试图确保（通常是历史上）处于不利地位的少数群体的预测效力的某些措施与大多数人口的预测效力相当。当一个校长试图采用一种群体公平的方法来取代另一个校长时，校长可能会面临那些认为他们可能会因这种转变而受到伤害的人的反对，而这反过来又可能会阻止采用。我们建议，一个潜在的缓解这一问题是，以确保一个组公平的模型也很受欢迎，在这个意义上说，对于大多数的目标人群，它产生了一个首选的分布结果相比，传统的模型。在本文中，我们表明，国家的艺术公平的学习方法往往是不受欢迎的，在这个意义上说。我们提出了几个有效的算法后处理现有的组公平的学习计划，以提高其普及，同时保持公平性。通过大量的实验，我们证明了所提出的后处理方法是非常有效的实践。
Group-fair learning methods typically seek to ensure that some measure of prediction efficacy for (often historically) disadvantaged minority groups is comparable to that for the majority of the population. When a principal seeks to adopt a group-fair approach to replace another, the principal may face opposition from those who feel they may be harmed by the switch, and this, in turn, may deter adoption. We propose that a potential mitigation to this concern is to ensure that a group-fair model is also popular, in the sense that, for a majority of the target population, it yields a preferred distribution over outcomes compared with the conventional model. In this paper, we show that state of the art fair learning approaches are often unpopular in this sense. We propose several efficient algorithms for postprocessing an existing group-fair learning scheme to improve its popularity while retaining fairness. Through extensive experiments, we demonstrate that the proposed postprocessing approaches are highly effective in practice.