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：审议民主理论上能带来更优的集体决策，但其根本受限于人类的注意力和带宽。尽管近期基于AI中介的审议通过综合多人输入扩大了参与规模，但对个体用户而言仍耗时费力。随着AI模型能力日益增强，AI系统不仅被部署为人类之间的审议中介，更开始代表人类参与其中——即AI代理代表人类用户进行审议。我们将这一范式称为"AI委托审议"。该范式虽为民主参与带来前所未有的规模，却引入了设计规范与对齐方面的全新挑战，且目前对此认知不足、理论薄弱。为实证研究这些动态机制，我们部署了面向AI委托审议的公共平台——哈贝摩尔特。我们从组织任何审议系统都需考量的三个维度（代表性、聚合性、修订性）评估其有效性，并基于观测结果揭示未来AI委托审议平台必须面对的设计抉择，从而为构建可扩展且可信赖的AI代表这一更广泛研究议程做出贡献。