Citizens’ Assemblies Are Upgrading Democracy: Fair Algorithms Are Part of the Program

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Citizens’ Assemblies Are Upgrading Democracy: Fair Algorithms Are Part of the Program
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Math helps to randomly select the fairest citizens’ assemblies since antiquity

In 1983 the Eighth Amendment to the Irish constitution enshrined an abortion ban that had prevailed in the nation for more than a century. Public opinion on the issue shifted in the new millennium, however, and by 2016 it was clear that a real debate could no longer be avoided. But even relatively progressive politicians had long steered clear of the controversy rather than risk alienating voters.

What’s more, unlike elected bodies, these assemblies are chosen to mirror the population, a property that political theorists refer to as descriptive representation. For example, a typical citizens’ assembly has a roughly equal number of men and women , whereas the average proportion of seats held by women in national parliaments worldwide was 26 percent in 2021—a marked increase from 12 percent in 1997 but still far from gender balance.

Then, the magistrate used a mechanism to reveal the marbles one by one. If the first marble was gold, the 10 citizens whose tokens appeared in the top row were added to the jury; if it was white, they were all dismissed. And so on, down the column of marbles and the rows of citizens: gold meant in; white meant out. To select a jury of 30 citizens, for example, the magistrate would include three gold marbles in the mix.

Until recently, the prevalent approach relied on what computer scientists call a “greedy algorithm.” This is a bit of misnomer because such an algorithm is really guilty of sloth rather than greed: It takes the action that seems best right now, without making an effort to understand what would work well in the long term. To select an assembly, a greedy algorithm adds volunteers one by one in a way that makes the most immediate progress toward filling the quotas.

To create a fairer algorithm, my collaborators and I adopt a holistic approach. Instead of considering volunteers one at a time, we consider the entire ensemble of potential assemblies, each of which meets all the demographic quotas. Each candidate assembly is given a lottery ticket that specifies its probability of being selected as the actual assembly. The probabilities are determined later, in such a way that they add up to 100 percent, and there’s only one winning ticket.

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