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Scaling Up Good Listening: An Assessment Framework for AI-Powered Mass Deliberation Models

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  • Ioana Grancea

    (Faculty of Philosophy and Social-Political Sciences, Alexandru Ioan Cuza University of Iași, Romania)

  • Viorel Ţuţui

    (Faculty of Philosophy and Social-Political Sciences, Alexandru Ioan Cuza University of Iași, Romania)

Abstract

The challenges of scaling up deliberative processes to mass audiences have long been highlighted by deliberative theorists. Apart from the difficulty of keeping content quality at a high enough level as more and more people get involved, the technical feasibility of mass participation in a structured form of deliberation has been a serious constraint. The development of digital platforms and AI systems are now making it technically possible to extend structured participation to wider audiences. This article addresses the following question: How can we ensure that good listening is scaled up in these new contexts? Drawing on an analytical framework based on recent contributions in the areas of deliberative democracy and AI, we evaluate the ability of current models of AI-powered mass deliberation to incentivize receptive, responsive, and apophatic listening. We further develop an assessment tool, the “Listening Incentives Score,” that can be used to assess whether AI-powered mass deliberation models provide participants with the adequate channels, facilitation, training, and systems of rewards and sanctions to incentivize them to engage in good listening.

Suggested Citation

  • Ioana Grancea & Viorel Ţuţui, 2025. "Scaling Up Good Listening: An Assessment Framework for AI-Powered Mass Deliberation Models," Media and Communication, Cogitatio Press, vol. 13.
    • Handle: RePEc:cog:meanco:v13:y:2025:a:9961
    DOI: 10.17645/mac.9961
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    References listed on IDEAS

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    1. Fishkin, James & Siu, Alice & Diamond, Larry & Bradburn, Norman, 2021. "Is Deliberation an Antidote to Extreme Partisan Polarization? Reflections on “America in One Room”," American Political Science Review, Cambridge University Press, vol. 115(4), pages 1464-1481, November.
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