AI Should Facilitate Democratic Deliberation at Scale
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Computer Science > Human-Computer Interaction
Title:AI Should Facilitate Democratic Deliberation at Scale
Abstract:AI systems can strengthen democracy by supporting deliberation at scale by addressing cognitive, social, platform-design, and market-driven frictions, while preserving human agency. Unlike proposals such as liquid democracy that restructure representation through vote delegation, in this position paper, we argue that AI-assisted deliberation offers a more promising path by lowering barriers to meaningful engagement without substituting machine judgment for human choice. Drawing on evidence from online deliberation platforms and experimental research, we identify four guiding principles: preserving agency and autonomy, encouraging mutual respect, promoting equality and inclusiveness, and augmenting rather than substituting active citizenship. We also address critical challenges, including alignment, sycophancy, training bias, and over-reliance on AI systems. We call on the machine learning community to develop deliberation-focused AI systems evaluated not on engagement metrics but on their capacity to facilitate informed, representative, and friction-robust discourse.
| Comments: | 15 pages, 2 figures, ICML 2026 |
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY) |
| ACM classes: | I.2.0; I.2.7; I.2.m; J.4; K.4.1; K.4.2; K.4.3 |
| Cite as: | arXiv:2609.20059 [cs.HC] |
| (or arXiv:2609.20059v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2609.20059
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Proceedings of the 43 rd International Conference on Machine Learning, Seoul, South Korea. PMLR 306, 2026 |
Submission history
From: José Ramón Enríquez [view email][v1] Thu, 17 Sep 2026 11:10:55 UTC (58 KB)
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