CIS Seminar: ” How Algorithms Can Support Deliberative Democracy”

Academics and political practitioners around the world are experimenting with a class of democratic innovations called deliberative mini-publics (DMs). In a DM, a panel of constituents convenes to deliberate about specific issues and make policy recommendations to traditional political decision-makers (e.g., legislators). Nearly all DMs rely on sortition – random selection – to choose the panelists. Sortition is often thought of as a simple lottery that chooses all constituents with equal probability. In practice, however, simple random selection fails to yield representative panels due to selection bias in who accepts invitations to participate. Many practitioners of DMs therefore sacrifice the pure equality embodied by a simple lottery, instead imposing quotas on socially salient groups and then “randomizing” within those constraints.

Engineering this randomization within user-specified quotas turns out to be technically demanding. My talk covers our algorithmic solution to this problem: a framework of optimization-based algorithms which, subject to such quotas, ensure individuals’ selection probabilities are as equal as possible, as measured by any convex function measuring equality (Fair Algorithms for Selecting Citizens’ Assemblies, Nature, ‘21).

After presenting our approach to this technical problem, I discuss my follow-up work demonstrating how the notion of equality we choose to optimize within this framework has implications for normative goals like fairness, transparency, and resistance to subversion. This includes a discussion of Leximin, the original instantiation of our framework, which has been adopted widely in practice and is available for public use at Panelot.org.