Habermolt: Delegating Deliberation to AI Representatives

• URL: http://arxiv.org/abs/2605.24413v3
• Date: Wed, 27 May 2026 10:45:17 GMT
• Title: Habermolt: Delegating Deliberation to AI Representatives
• Abstract summary: Deliberative democracy arguably leads to better collective decisions, but is fundamentally constrained by human attention and bandwidth.<n>Recent AI-mediated deliberations scale participation by synthesizing inputs from many humans, but remain time-intensive for individual users.<n>As AI models become increasingly capable, AI systems are being deployed not only to mediate deliberation between humans, but to represent humans in it.
• Score: 4.882694170903341
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deliberative democracy arguably leads to better collective decisions, but is fundamentally constrained by human attention and bandwidth. While recent AI-mediated deliberations scale participation by synthesizing inputs from many humans, they remain time-intensive for individual users. As AI models become increasingly capable, AI systems are being deployed not only to mediate deliberation between humans, but to represent humans in it: where AI agents deliberate on behalf of human users. We call this paradigm AI-delegated deliberation. While it promises unprecedented scale for democratic participation, it introduces qualitatively new design and alignment challenges that are poorly understood and under-theorized. To study these dynamics empirically, we deploy Habermolt, a public platform for AI-delegated deliberation. We evaluate its effectiveness along three dimensions that we use to organize any deliberative system: representation, aggregation, and revision. We use these observations to illuminate the design decisions future AI-delegated deliberation platforms must confront, contributing to the broader research agenda for scalable yet trustworthy AI representatives.

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