This paper uses 119 conversations with 100 or more participants and an extensive data export, drawn from a wider set of 271 collected processes, to ask what determines the output of such a process.
Abstract
Polis is a popular democratic innovation tool that allows asynchronous citizen engagement through atomic statements: short statements that together describe a complex question, inviting the citizen to vote Agree or Disagree on each. This paper uses 119 conversations with 100 or more participants and an extensive data export, drawn from a wider set of 271 collected processes. The paper asks what determines the output of such a process. Three parties shape the result. The developer of the platform has made important design choices that restrict the outcome: the number of groups the platform is able to report (restricted to 2--5) and which statements are prioritized. The convener defines the assignment: the initial statements that set the tone, the policy that accepts or rejects new statements and who can be invited. Finally, the participant works within these boundaries. With access to less than half of the generated statements, they end up responding to more statements when their conversation seems to have an achievable number of statements to complete, than when they are presented with more statements. Due to choices such as warm path clustering, the exported resulting clustering cannot be reproduced based on the voting data. Conveners may want to re-analyse their own conversations once the process is closed, to consider the data in its entirety, and make their own analysis priorities explicit.
It is argued that LLM-based AI systems answer political questions for hundreds of millions of people is a set of policies over whom to answer, what to say, and whether to engage at all, conditional on the topic and what the system knows about the user.
This exploratory qualitative study examines how students' expectations of one process-capture platform, Turnitin Clarity, compared with their experience of using it are compared with their experience of using the platform.
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Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
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MIT News · Artificial Intelligence· news.mit.eduSep 30, 2026
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