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.

翻译：审议民主理论上能促成更优的集体决策，但本质上受限于人类的注意力与带宽。尽管近期基于AI中介的审议通过综合多人输入扩展了参与规模，但对个体用户而言仍耗时颇多。随着AI模型能力日益增强，AI系统不仅被部署为人类审议的中介，更开始直接代表人类参与审议——即AI智能体代表人类用户进行商议。我们称此范式为"AI委托式审议"。该范式虽为民主参与带来前所未有的规模化潜力，却也引入了设计偏差与对齐挑战等新型质性问题，这些挑战尚未得到充分理解与理论化。为实证研究此类动态机制，我们部署了Habermolt——一个面向AI委托式审议的公开平台。我们从表征、聚合与修订三个维度评估其有效性，这三个维度亦是我们用以组织任何审议系统的框架。基于观测结果，我们揭示未来AI委托式审议平台必须面对的设计抉择，为构建可扩展且可信赖的AI代表这一更广泛研究议程贡献力量。