Sortition is a political system in which decisions are made by panels of randomly selected citizens. The process for selecting a sortition panel is traditionally thought of as uniform sampling without replacement, which has strong fairness properties. In practice, however, sampling without replacement is not possible since only a fraction of agents is willing to participate in a panel when invited, and different demographic groups participate at different rates. In order to still produce panels whose composition resembles that of the population, we develop a sampling algorithm that restores close-to-equal representation probabilities for all agents while satisfying meaningful demographic quotas. As part of its input, our algorithm requires probabilities indicating how likely each volunteer in the pool was to participate. Since these participation probabilities are not directly observable, we show how to learn them, and demonstrate our approach using data on a real sortition panel combined with information on the general population in the form of publicly available survey data.

中文翻译：

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中和分选抽样中的自选择偏差

Sortition 是一种政治系统，其中决策由随机选择的公民组成的小组做出。选择抽签小组的过程传统上被认为是无替换的统一抽样，具有很强的公平性。然而，在实践中，没有替换的抽样是不可能的，因为只有一小部分代理愿意在受邀时参与小组，并且不同的人口群体以不同的比率参与。为了仍然生成组成类似于总体的面板，我们开发了一种采样算法，该算法可以在满足有意义的人口配额的同时恢复所有代理的接近相等的表示概率。作为其输入的一部分，我们的算法需要概率来表明池中每个志愿者参与的可能性。