发表机构
Stanford Graduate School of Business; Stanford Institute for Human-Centered AI; MIT Media Lab（斯坦福大学商学院; 斯坦福大学以人为本人工智能研究院; 麻省理工学院媒体实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文主张AI应通过降低参与门槛、保留人类能动性来促进大规模民主协商，并提出四项原则及挑战，呼吁开发以协商为中心的AI系统。
AI 中文摘要
AI系统可以通过解决认知、社会、平台设计和市场驱动的摩擦，同时保留人类能动性，来支持大规模协商，从而加强民主。与液体民主等通过投票委托重构代表制的提案不同，在这篇立场论文中，我们认为AI辅助协商提供了一条更有前景的路径，它降低了有意义参与的门槛，而不以机器判断替代人类选择。基于在线协商平台和实验研究的证据，我们确定了四项指导原则：保留能动性和自主性、鼓励相互尊重、促进平等和包容性，以及增强而非替代积极的公民参与。我们还解决了关键挑战，包括对齐、谄媚、训练偏见以及对AI系统的过度依赖。我们呼吁机器学习社区开发以协商为中心的AI系统，这些系统的评估不应基于参与度指标，而应基于其促进知情、具有代表性且对摩擦具有鲁棒性的讨论的能力。
英文摘要
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.
Comments15 pages, 2 figures, ICML 2026
Journal refProceedings of the 43 rd International Conference on Machine Learning, Seoul, South Korea. PMLR 306, 2026
