Firstly, we must continually question the underlying assumptions, potential pitfalls, risks, and possible unintended adverse effects of introducing AI into democratic processes. Not the least by always checking and refining LLM outputs with real people, or we risk falling into the fallacies and risks of democracy in silica.

Secondly, our process is inherently and somewhat intimately social. While this is by design, we observe that a significant portion of the population self selects out of such explicitly social interactions with strangers. These are similar issues faced by in-person citizens assemblies, where a small portion of the participants may need repeated encouragement before they share their opinions and gain confidence. While human facilitators were on call to help during the experiment, looking into active facilitation, coaching and aftercare for more sensitive participants may be crucial when deploying Common Ground.

Common Ground can be conceptualised as a multi-player variant of Pol.is. Instead of voting on Statements in isolation, we match participants into small groups of three people where they are encouraged to deliberate over the Statements they vote on, and where an AI moderator powered by GPT4 synthesises new Statements from the content of their discussion.