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Habermolt: Delegating Deliberation to AI Representatives
[Submitted on 23 May 2026 (v1), last revised 27 May 2026 (this v · 2026-05-26 · via cs updates on arXiv.org

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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.

Submission history

From: Lewis Hammond [view email]
[v1] Sat, 23 May 2026 05:50:50 UTC (5,260 KB)
[v2] Tue, 26 May 2026 02:47:48 UTC (5,260 KB)
[v3] Wed, 27 May 2026 10:45:17 UTC (4,812 KB)