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◆ Collective Intelligence2026-04-01· Least-squares function approximation

AI and the future of digital public squares

Beth Goldberg, Diana Acosta-Navas, Michiel A. Bakker, Ian Beacock, Matt Botvinick, Prateek Buch, Renée DiResta, Nandika Donthi, Nathanael Fast, Ravi Iyer, Zaria Jalan, Andrew Konya, Grace Kwak Danciu, Hélène Landemore, Alice Marwick, Carl Miller, Aviv Ovadya, Emily Saltz, Lisa Schirch, Dalit Shalom, Divya Siddarth, Felix Sieker, C. Small, Jonathan Stray, Audrey Tang, Michael Tessler, Amy X. Zhang

原始摘要(英文原文)· Original abstract
Two substantial technological advances have reshaped the public square in recent decades: first, the advent of the internet and second, the recent introduction of large language models (LLMs). LLMs offer opportunities to reimagine and redesign more decentralized, participatory online spaces that can be used to facilitate deliberative dialogues at scale, but LLMs without proper governance can also exacerbate societal schisms. Here, we explore four applications of LLMs to improve digital public squares: collective dialogue systems, bridging systems, community moderation, and proof-of-humanity systems. Building on the input from over 70 civil society experts and technologists, we argue that LLMs both afford promising opportunities to shift the paradigm for conversations at scale and pose distinct risks for digital public squares. Given that people are shaped through their interactions in public squares, there is an important opportunity to build individual capacities and collective cohesion by transforming these digital public spaces. We lay out an agenda for future research and investments in AI that will strengthen digital public squares and safeguard against potential misuses of AI.
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AI and the future of digital public squares — 科研速览 Science Skim