Citizens’ assemblies, randomly selected panels contributing to policy questions, necessitate selection algorithms balancing representativeness and equal selection probabilities. Existing algorithms prioritize representativeness, neglecting equal opportunity. This paper introduces algorithms achieving maximal fairness by selecting representative panels while making individual selection probabilities as close to equal as possible. An implemented algorithm, already used globally for over 40 assemblies, demonstrates substantially fairer selection probabilities than previous methods.

Bailey Flanigan, Paul Gölz, Anupam Gupta, Brett Hennig, Ariel D. Procaccia