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.
| Subjects: | Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2605.24413 [cs.CY] |
| (or arXiv:2605.24413v1 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24413 arXiv-issued DOI via DataCite (pending registration) |
From: Joseph Low [view email]
[v1]
Sat, 23 May 2026 05:50:50 UTC (5,260 KB)
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