AI brings citizens’ assemblies into the 21st century

Democracy in ancient Athens was very different from today’s democracies. Instead of elections, most positions – including those in the legislature, boards of directors and magistrates – were filled by volunteer citizens, selected by lot. These assemblies of citizens drew up, debated and voted on laws; made important foreign policy decisions; and controlled military budgets.

Today, citizens’ assemblies are making a comeback. In 2019 and 2020, citizens’ assemblies in France and the UK came together to develop action to tackle climate change. Citizens’ assemblies in Ireland led to changes to the Irish constitution that legalized abortion and same-sex marriage.

One of the greatest challenges in organizing these assemblies – both in ancient times and today – is deciding who should serve. The assembly must be representative of the entire population. But the selection should be random – ideally all volunteers have an equal chance of being chosen.

To balance these two goals, the ancient Athenians used a rudimentary machine called a kleroterion, which randomly selected groups of volunteers from different tribes. Today, a team of computer scientists has come up with a 21st century solution.

Now, a team of computer scientists from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and Carnegie Mellon University, in collaboration with a practitioner from and the Sortition Foundation, have designed a screening process for assembly that satisfies representation and fairness. simultaneously.

This article was published in Nature.

“Ideally, a citizens’ assembly acts like a microcosm of society,” said Ariel Procaccia, Gordon McKay professor of computer science at SEAS and co-author of the study. “Achieving this goal in practice, however, depends on how the members of the assembly are chosen. “

“First, we have to ask ourselves how do we even think about fairness in the context of panel selection, and then how do we formalize it so that everyone has a fair chance,” said Bailey Flanigan, a graduate student from Carnegie. Mellon University and study co-author.

The research team examined a typical two-step assembly selection process. In the first step, thousands of randomly selected people are invited to participate. The final assembly is chosen from the pool of volunteers using a selection algorithm. However, the volunteer pool tends not to be representative of the population as a whole because certain groups, such as those who are more educated, are more likely to volunteer.

“Giving all volunteers exactly equal probabilities is usually impossible to do while still meeting demographic quotas,” said Paul Gölz, Carnegie Mellon graduate student and co-author of the article. “Our screening algorithm finds a panel that meets the quotas while giving potential participants the same chance as possible of being selected.”

It does this by calculating a distribution over many panels, all of which meet the quota requirements, and then randomly drawing a panel from that distribution. A distribution of panels is then chosen so that the minimum probability of a volunteer appearing on the panel is mathematically as high as possible.

This open source algorithm has already been used to select over 40 citizen assemblies around the world, by organizations in countries such as Denmark, Germany, the United States, Belgium and the United Kingdom. Procaccia, along with his co-authors and Gili Rusak from Stanford University, has developed a website called, which makes their selection algorithm available for free.

Going forward, researchers will continue to work with practitioners to learn from their experience on how these new selection algorithms can be made even more useful.

“We are excited to explore new ways in which mathematics and computer science can contribute to the practice of democracy,” Procaccia said.

Flanigan B, Gölz P, Gupta A, Hennig B, Procaccia AD. Fair algorithms for selecting citizens’ assemblies. Nature. 2021: 1-5. doi: 10.1038 / s41586-021-03788-6

This article was republished from the following materials. Note: The material may have been modified for its length and content. For more information, please contact the cited source.

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