Globally, there has been a recent surge in ‘citizens’ assemblies’¹, which are a form of civic participation in which a panel of randomly selected constituents contributes to questions of policy. The random process for selecting this panel should satisfy two properties. First, it must produce a panel that is representative of the population. Second, in the spirit of democratic equality, individuals would ideally be selected to serve on this panel with equal probability^2,3. However, in practice these desiderata are in tension owing to differential participation rates across subpopulations^4,5. Here we apply ideas from fair division to develop selection algorithms that satisfy the two desiderata simultaneously to the greatest possible extent: our selection algorithms choose representative panels while selecting individuals with probabilities as close to equal as mathematically possible, for many metrics of ‘closeness to equality’. Our implementation of one such algorithm has already been used to select more than 40 citizens’ assemblies around the world. As we demonstrate using data from ten citizens’ assemblies, adopting our algorithm over a benchmark representing the previous state of the art leads to substantially fairer selection probabilities. By contributing a fairer, more principled and deployable algorithm, our work puts the practice of sortition on firmer foundations. Moreover, our work establishes citizens’ assemblies as a domain in which insights from the field of fair division can lead to high-impact applications.

在全球范围内，最近出现了“公民”大会¹的激增，这是一种公民参与形式，由随机选择的成员组成的小组讨论政策问题。选择此面板的随机过程应满足两个属性。首先，它必须产生一个代表人口的面板。其次，本着民主平等的精神，理想情况下，个人会以相同的概率^2,3被选为该小组成员。然而，在实践中，由于不同亚群的参与率不同，这些需求者处于紧张状态^4,5. 在这里，我们应用公平划分的想法来开发最大可能同时满足两个需求的选择算法：我们的选择算法选择具有代表性的面板，同时选择在数学上尽可能接近相等的个体，用于许多“接近平等”的指标'。我们实施的一种此类算法已被用于选择全球 40 多个公民集会。正如我们使用来自十个公民集会的数据所展示的那样，在代表先前技术水平的基准上采用我们的算法会导致选择概率更加公平。通过提供更公平、更有原则和可部署的算法，我们的工作将抽签实践建立在更坚实的基础上。而且，