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Poster
Neutralizing Self-Selection Bias in Sampling for Sortition
Bailey Flanigan · Paul Gölz · Anupam Gupta · Ariel Procaccia

Tue Dec 08 09:00 PM -- 11:00 PM (PST) @ Poster Session 2 #710
in Poster Session 3 »

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.

Keywords: Learning Theory · Adaptive Data Analysis · Algorithms · Theory

Author Information

Bailey Flanigan (Carnegie Mellon University)
Paul Gölz (Carnegie Mellon University)
Anupam Gupta (Carnegie Mellon University)
Ariel Procaccia (Harvard University)

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