Habermolt: Delegating Deliberation to AI Representatives
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866913168028073984 |
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| author | Low, Joseph Duys, Oscar Formanek, Claude Bakker, Michiel Hammond, Lewis |
| author_facet | Low, Joseph Duys, Oscar Formanek, Claude Bakker, Michiel Hammond, Lewis |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_24413 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Habermolt: Delegating Deliberation to AI Representatives Low, Joseph Duys, Oscar Formanek, Claude Bakker, Michiel Hammond, Lewis Computers and Society Human-Computer Interaction Multiagent Systems 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. |
| title | Habermolt: Delegating Deliberation to AI Representatives |
| topic | Computers and Society Human-Computer Interaction Multiagent Systems |
| url | https://arxiv.org/abs/2605.24413 |