Can AI make democracy more equitable?

Can AI make democracy more equitable?

Democracy in ancient Athens looked quite different from today’s democracy. Instead of elections, most positions, including the legislature, the governing council, and justice of the peace, were filled with citizen volunteers selected by random lottery. These civic meetings drafted, debated, and passed the law. Made major foreign policy decisions. I managed the military budget.


Today, civic meetings are reviving. Citizens’ rallies in France and the United Kingdom were held in 2019 and 2020 to draft measures to address climate change. Citizens’ rallies in Ireland have led to changes in the Irish Constitution that legalize abortion and same-sex marriage.

One of the biggest challenges in organizing these meetings is deciding who to serve, both ancient and today. Parliament needs to represent the population as a whole. However, the selection must be random. Ideally, all volunteers should be selected with the same probability.

To balance these two goals, the ancient Athenians used a basic machine called the kleroterion. It randomly selected a panel of volunteers from different tribes. Today, a team of computer scientists have devised solutions for the 21st century.

Currently, Harvard John A. Paulson’s School of Engineering and Applied Sciences (SEAS) and a team of computer scientists at Carnegie Mellon University have worked with Sortie Foundation practitioners to design an assembly selection process that meets expression and fairness. at the same time.

This paper was published in Nature..

“Ideally, civic meetings act as a microcosm of society,” said Ariel Procatcha, professor of computer science at SEAS and co-author of the study. “But whether this goal is actually achieved depends on how assembly members are selected.”

Bailey Flanigan, a graduate student at Carnegie Mellon University, said, “First, how to think about fairness in the context of panel selection, and then how to formalize it so that everyone has a fair chance. I need to ask. ” Co-author of research with Mellon University. “

The research team examined a typical two-step assembly selection process. In the first stage, thousands of randomly selected people are invited to participate. The final assembly is selected from a pool of volunteers using a selection algorithm.However, the volunteer pool is for certain groups, such as more educated groups. volunteer..

“Giving exactly equal probabilities to all volunteers is generally impossible to do while meeting demographic quotas,” said Paul Gertz. graduate student Co-author of a treatise at Carnegie Mellon University. “Our selection algorithm finds a panel that meets the quota, giving potential participants as much chance of being selected as possible.”

This is done by calculating a distribution across many panels, all of which meet quota requirements, and drawing panels randomly from this distribution. The panel distribution is then chosen so that the probability that volunteers will appear on the panel is mathematically as high as possible.

This open source algorithm has already been used by organizations in countries such as Denmark, Germany, the United States, Belgium and the United Kingdom to select more than 40 civic meetings around the world. Procaccia, along with his co-author and Stanford University’s Gili Rusak, has developed a website called This makes the selection algorithm available for free.

Researchers will continue to work with practitioners to learn from their experience with these new methods. Choice You can make the algorithm even more convenient.

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

Why We Need a Global Citizens’ Rally on Gene Editing

For more information:
Flanigan, B. et al, a fair algorithm for selecting civic meetings, Nature (2021). DOI: 10.1038 / s41586-021-03788-6

Provided by
Harvard John A. Paulson School of Engineering and Applied Science

Quote: Can AI make democracy more equitable? (August 4, 2021) Obtained August 5, 2021 from

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