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,andthatitgeneratespolicyguidelineswithstrongpublicsupportacrossdemographicdivides.Wemeasure75−81guidelinesoverall,andnolessthan70−75spanningage,gender,religion,race,education,andpoliticalparty.Overall,thisworkdemonstratesanend−to−endproofofconceptforaprocesswebelievecanhelpAIlabsdevelopcommon−groundpolicies,governingbodiesbreakpoliticalgridlock,anddiplomatsacceleratepeacedeals.