We design and test an efficient democratic process for developing policies that reflect informed public will. The process combines AI-enabled collective dialogues that make deliberation democratically viable at scale with bridging-based ranking for automated consensus discovery. A GPT4-powered pipeline translates points of consensus into representative policy clauses from which an initial policy is assembled. The initial policy is iteratively refined with the input of experts and the public before a final vote and evaluation. We test the process three times with the US public, developing policy guidelines for AI assistants related to medical advice, vaccine information, and wars & conflicts. We show the process can be run in two weeks with 1500+ participants for around $10,000, and that it generates policy guidelines with strong public support across demographic divides. We measure 75-81% support for the policy guidelines overall, and no less than 70-75% support across demographic splits spanning age, gender, religion, race, education, and political party. Overall, this work demonstrates an end-to-end proof of concept for a process we believe can help AI labs develop common-ground policies, governing bodies break political gridlock, and diplomats accelerate peace deals.

翻译：我们设计并测试了一种高效的民主流程，用于制定反映公众知情意愿的政策。该流程结合了由人工智能驱动的集体对话（使大规模民主审议在技术上可行）与基于桥接排序的自动共识发现机制。通过GPT4驱动的流水线，将共识点转化为具有代表性的政策条款，并由此组装形成初始政策草案。该草案经过专家与公众的迭代优化后，最终进行投票与评估。我们以美国公众为对象进行了三轮测试，分别制定关于医疗建议、疫苗信息及战争冲突的AI助手政策指南。实验表明，该流程可在两周内以约一万美元的成本完成，参与者超过1500人，并制定出跨越人口统计差异的获得广泛公众支持的政策指南。总体政策指南支持率达75-81%，且在不同年龄、性别、宗教、种族、教育背景及政治派别的人口统计分组中，支持率均不低于70-75%。本项工作完整呈现了该流程的概念验证，我们相信该流程有助于AI实验室制定共识性政策、帮助治理机构打破政治僵局，并协助外交官加速和平协议的达成。