# Democratic Policy Development using Collective Dialogues and AI

arXiv: https://arxiv.org/abs/2311.02242 · HTML: https://mlsanity.com/paper/2311.02242/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2023.

## Built on by (most cited first, 17 of 17)

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- [Chain of Alignment: Integrating Public Will with Expert Intelligence for Language Model Alignment](https://mlsanity.com/paper/2411.10534/) (2024)
- [An AI-Powered Framework for Analyzing Collective Idea Evolution in Deliberative Assemblies](https://mlsanity.com/paper/2509.12577/) (2025)
- [Finding Common Ground in a Sea of Alternatives](https://mlsanity.com/paper/2603.16751/) (2026)
- [PolicyPad: Collaborative Prototyping of LLM Policies](https://mlsanity.com/paper/2509.19680/) (2025)
- [Can AI mediation improve democratic deliberation?](https://mlsanity.com/paper/2601.05904/) (2026)
- ["Better Ask for Forgiveness than Permission": Practices and Policies of AI Disclosure in Freelance Work](https://mlsanity.com/paper/2603.07459/) (2026)
- [Internal Pluralism and the Limits of Pairwise Comparisons](https://mlsanity.com/paper/2607.02672/) (2026)
- [AI Should Facilitate Democratic Deliberation at Scale](https://mlsanity.com/paper/2609.20059/) (2026)
- [Social Choice Foundations for Simulation-Augmented Generation](https://mlsanity.com/paper/2609.38287/) (2026)

## Abstract (arXiv)

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.