Polis:: Scaling Deliberation by Mapping High Dimensional Opinion Spaces

• Christopher Small ^[2] ; Michael Bjorkegren ^[2] ; Timo Erkkilä ^[2] ; Lynette Shaw ^[1] ; Colin Megill ^[2]
  1. [1] University of Michigan–Ann Arbor

     University of Michigan–Ann Arbor

     City of Ann Arbor, Estados Unidos

     
  2. [2] Polis Technologies Inc
• Localización: Recerca: revista de pensament i analisi, ISSN 1130-6149, Nº. 26, 2, 2021 (Ejemplar dedicado a: Democracia en la era de la Inteligencia Artificial), págs. 1-26
• Idioma: inglés
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• Resumen

  • Deliberative and participatory approaches to democracy seek to directly include citizens in decision-making and agenda-setting processes. These methods date back to the very foundations of democracy in Athens, where regular citizens shared the burden of governance and deliberated every major issue. However, thinkers at the time rightly believed that these methods could not function beyond the scale of the city-state, or polis. Representative democracy as an innovation improved on the scalability of collective decision making, but in doing so, sacrificed the extent to which regular citizens could participate in deliberation. Modern technology, including advances in computational power, machine learning algorithms, and data visualization techniques, presents a unique opportunity to scale out deliberative processes. Here we describe Polis, an open source web application capable of collecting and synthesizing feedback from people in a scalable and distributed fashion. Polis has shown itself capable of building shared understanding, disincentivizing counterproductive behavior (trolling), and cultivating points of consensus. It has done this in the context of journalistic and academic research, and directly as part of decision-making bodies at local and national levels, directly affecting legislation. These results demonstrate that deliberative processes can be scaled up beyond the constraints of in-person gatherings and small groups.