This paper outlines the language ideological work necessary to producing and understanding something to be “consensus,” among both the developers of LLM-based platforms for promoting democratic participation, and for their users.
Long abstract:
Democracy is one among the many latest domains of AI experimentation. Both developers and policymakers see AI as a way not only to scale-up citizen participation, but also to “improve” democracy by finding consensus within even seemingly polarized opinions. One commonly used tool is the open-source platform Polis, created by The Computational Democracy Project, an American non-profit organization. Participants in projects using Polis can submit their own comments in response to an open-ended prompt and rank others’ comments. Although Polis’s algorithms are currently language-agonistic, developers have begun to try to integrate LLMs, with the hope, among other things, of being able to find consensus more quickly through generating statements that it suspects a majority of participants will agree with and predicting how participants would vote on comments they have not seen.
This paper outlines the language ideological work necessary to producing and understanding something to be “consensus.” I ask what consensus is for the individuals and organizations behind Polis and similar platforms as they move towards adopting LLMs, and the expectations of organizations in the Netherlands experimenting with these tools. Preliminary research suggests that Polis’s promise of being able to find consensus where humans cannot is undermined because what it produces is not then recognized as consensus by its users.