Platforms for online civic participation rely heavily on methods for condensing thousands of comments into a relevant handful, based on whether participants agree or disagree with them. These methods should guarantee fair representation of the participants, as their outcomes may affect the health of the conversation and inform impactful downstream decisions. To that end, we draw on the literature on approval-based committee elections. Our setting is novel in that the approval votes are incomplete since participants will typically not vote on all comments. We prove that this complication renders non-adaptive algorithms impractical in terms of the amount of information they must gather. Therefore, we develop an adaptive algorithm that uses information more efficiently by presenting incoming participants with statements that appear promising based on votes by previous participants. We prove that this method satisfies commonly used notions of fair representation, even when participants only vote on a small fraction of comments. Finally, an empirical evaluation on real data shows that the proposed algorithm provides representative outcomes in practice.
翻译:在线公民参与平台在很大程度上依赖基于参与者是否同意或不同意的方法,将数千条意见浓缩成一个相关小块意见的方法。这些方法应当保证参与者的公平代表性,因为其结果可能影响对话的健康和影响深远的下游决定。为此,我们借鉴关于批准委员会选举的文献。我们的设置是新颖的,因为批准票是不完整的,因为与会者通常不会对所有意见进行投票。我们证明,这种复杂使非适应性算法在他们必须收集的信息数量上变得不切实际。因此,我们开发了一种适应性算法,通过向即将到任的参与者提供根据前几位参与者的投票看来有希望的声明来更有效地使用信息。我们证明,这一方法满足了通常使用的公平代表性概念,即使参与者只对一小部分评论进行投票。最后,对真实数据进行的经验评估表明,拟议的算法在实践中提供了有代表性的结果。