HABERMOLT: Delegating Deliberation to AI Representatives

Joseph Low, Oscar Duys, Juan Claude Formanek, Michiel A. Bakker, Lewis Hammond

Keywords: artificial intelligence, collective intelligence, AI-delegated deliberation, deliberative democracy

TL;DR: We introduce AI-delegated deliberation, deploy HABERMOLT as a public platform instantiating this paradigm, and empirically surface key design challenges around representation, aggregation, and revision.

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.

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Submission Number: 212