We study multiwinner voting problems when there is an additional requirement that the selected committee should be fair with respect to attributes such as gender, ethnicity, or political parties. Every setting of an attribute gives rise to a group, and the goal is to ensure that each group is neither over nor under represented in the selected committee. Prior work has largely focused on designing specialized score functions that lead to a precise level of representation with respect to disjoint attributes (e.g., only political affiliation). Here we propose a general algorithmic framework that allows the use of any score function and can guarantee flexible notions of fairness with respect to multiple, non-disjoint attributes (e.g., political affiliation and gender). Technically, we study the complexity of this constrained multiwinner voting problem subject to group-fairness constraints for monotone submodular score functions. We present approximation algorithms and hardness of approximation results for various attribute set structures and score functions.

我们介绍了一种算法框架，用于处理多获胜者投票问题，并在特定属性方面保持公平。该框架可以满足多个非不相交属性的公平性要求，并且可以指定一个评分函数。我们研究了单调和次模评分函数的计算复杂度，针对各种属性组结构和评分函数类型提出了几种近似算法和匹配的近似难度结果。我们还进行了模拟实验，结果表明添加公平约束可能不会对分数产生显著影响。

具有公平约束的多胜方投票