Habermolt: Delegating Deliberation to AI Representatives
This work addresses the problem of scaling democratic participation by delegating deliberation to AI, but it is an early-stage exploration without concrete performance numbers.
The paper introduces AI-delegated deliberation, where AI agents represent humans in democratic deliberation, and presents Habermolt, a public platform for this paradigm. It evaluates the platform along representation, aggregation, and revision dimensions, highlighting design challenges for scalable and trustworthy AI representatives.
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.