Group Fairness in Multiwinner Voting
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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.
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