Fairness in multiwinner elections is studied in varying contexts. For instance, diversity of candidates and representation of voters are both separately termed as being fair. A common denominator to ensure fairness across all such contexts is the use of constraints. However, across these contexts, the candidates selected to satisfy the given constraints may systematically lead to unfair outcomes for historically disadvantaged voter populations as the cost of fairness may be borne unequally. Hence, we develop a model to select candidates that satisfy the constraints fairly across voter populations. To do so, the model maps the constrained multiwinner election problem to a problem of fairly allocating indivisible goods. We propose three variants of the model, namely, global, localized, and inter-sectional. Next, we analyze the model's computational complexity, and we present an empirical analysis of the utility traded-off across various settings of our model across the three variants and discuss the impact of Simpson's paradox using synthetic datasets and a dataset of voting at the United Nations. Finally, we discuss the implications of our work for AI and machine learning, especially for studies that use constraints to guarantee fairness.
翻译:对多赢选举的公平性进行了不同背景的研究,例如,候选人的多样性和选民的代表权被分别称为公平性。确保所有此类背景的公平性的共同标准是使用制约。然而,在这些背景中,为满足特定限制而选定的候选人可能会系统地导致历史上处于不利地位的选民群体获得不公平的结果,因为公平性的代价可能不平均地承担。因此,我们开发了一个选择候选人的模式,以公平地满足选民群体之间的限制。为了做到这一点,模型将受限制的多赢选举问题描述为公平分配不可分割货物的问题。我们提出了模式的三个变体,即全球、本地和跨部门。我们分析了模型的计算复杂性,然后我们提出了对我们模式在三个变体中不同环境之间的实用性交易进行的经验分析,并讨论了辛普森悖论的影响,使用了综合数据集和联合国投票数据集。最后,我们讨论了我们的工作对大赦国际和机器学习的影响,特别是对使用制约来保证公平性的研究的影响。