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arXiv · 2605.24413

Habermolt: Delegating Deliberation to AI Representatives

Joseph Low·Oscar Duys·Claude Formanek·Michiel Bakker·Lewis Hammond
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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.

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Joseph Low, Oscar Duys, Claude Formanek, Michiel Bakker, Lewis Hammond. 2026-05-23. Habermolt: Delegating Deliberation to AI Representatives. https://arxiv.org/abs/2605.24413

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