The model is now gaining traction around the world. In the UK and France, for example, citizens’ assemblies have been convened to deliberate responses to climate change. But selecting the members of these bodies is a complicated task. Ideally, citizens’ assemblies should be both representative and randomly selected. Balancing these two requirements is challenging as the volunteers tend to be unrepresentative of the whole population. A team of computer scientists from Harvard and Carnegie Mellon universities has devised a potential solution: selection algorithms.
Computing democracy
The team’s system finds panels that satisfy quota requirements and give potential members as equal a chance of selection as is mathematically possible. The algorithm first constructs a set of quota-dependent panels. These are developed by iteratively building an “optimal portfolio” of panels and computing the fairest distribution of participants. A single panel is then randomly drawn from the distribution. The open-source algorithm has already been used to select more than 40 citizens’ assemblies around the world. In Michigan, the system was used to pick a panel of 30 residents to make recommendations about COVID-19. The process was a success, according to a June report by Fast Company: The researchers will now explore new ways that computer science can contribute to democratic practices. In a time of declining respect for professional politicians, the team’s algorithms could enhance an alternative form of representative democracy. You can read the open-access study paper in Nature. Greetings Humanoids! Did you know we have a newsletter all about AI? You can subscribe to it right here.
