# AI-Enhanced Deliberative Democracy and the Future of the Collective Will

arXiv: https://arxiv.org/abs/2503.05830 · HTML: https://mlsanity.com/paper/2503.05830/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 130 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 24 of 24)

- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Benchmarking Large Language Models for News Summarization](https://mlsanity.com/paper/2301.13848/) (2023)
- [A Survey of Hallucination in Large Foundation Models](https://mlsanity.com/paper/2309.05922/) (2023)
- [From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models](https://mlsanity.com/paper/2305.08283/) (2023)
- [The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation](https://mlsanity.com/paper/2301.01768/) (2023)
- [Gender bias and stereotypes in Large Language Models](https://mlsanity.com/paper/2308.14921/) (2023)
- [Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models](https://mlsanity.com/paper/2402.16786/) (2024)
- [Homogenization Effects of Large Language Models on Human Creative Ideation](https://mlsanity.com/paper/2402.01536/) (2024)
- [Collective Constitutional AI: Aligning a Language Model with Public Input](https://mlsanity.com/paper/2406.07814/) (2024)
- [Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voters](https://mlsanity.com/paper/2410.24190/) (2024)
- [Utility Engineering: Analyzing and Controlling Emergent Value Systems in AIs](https://mlsanity.com/paper/2502.08640/) (2025)
- [Strong Model Collapse](https://mlsanity.com/paper/2410.04840/) (2024)
- [What are human values, and how do we align AI to them?](https://mlsanity.com/paper/2404.10636/) (2024)
- [Language Agents as Digital Representatives in Collective Decision-Making](https://mlsanity.com/paper/2502.09369/) (2025)
- [Regurgitative Training: The Value of Real Data in Training Large Language Models](https://mlsanity.com/paper/2407.12835/) (2024)
- [Lies, Damned Lies, and Distributional Language Statistics: Persuasion and Deception with Large Language Models](https://mlsanity.com/paper/2412.17128/) (2024)
- [Deception and Manipulation in Generative AI](https://mlsanity.com/paper/2401.11335/) (2024)
- [Using Collective Dialogues and AI to Find Common Ground Between Israeli and Palestinian Peacebuilders](https://mlsanity.com/paper/2503.01769/) (2025)
- [How will advanced AI systems impact democracy?](https://mlsanity.com/paper/2409.06729/) (2024)
- [Deliberative Technology for Alignment](https://mlsanity.com/paper/2312.03893/) (2023)
- [Faster Peace via Inclusivity: An Efficient Paradigm to Understand Populations in Conflict Zones](https://mlsanity.com/paper/2311.00816/) (2023)
- [Intention Is All You Need](https://mlsanity.com/paper/2410.18851/) (2024)

## Built on by (most cited first, 4 of 4)

- [Generating Fair Consensus Statements with Social Choice on Token-Level MDPs](https://mlsanity.com/paper/2510.14106/) (2025)
- [Embeddings for Preferences, Not Semantics](https://mlsanity.com/paper/2605.08360/) (2026)
- [Snowveil: A Framework for Decentralised Preference Discovery](https://mlsanity.com/paper/2512.18444/) (2025)
- [Toward Collective-Centric Evaluation of Preference Inference for Participatory Democracy](https://mlsanity.com/paper/2609.02990/) (2026)

## Abstract (arXiv)

This article unpacks the design choices behind longstanding and newly proposed computational frameworks aimed at finding common grounds across collective preferences and examines their potential future impacts, both technically and normatively. It begins by situating AI-assisted preference elicitation within the historical role of opinion polls, emphasizing that preferences are shaped by the decision-making context and are seldom objectively captured. With that caveat in mind, we explore AI-based democratic innovations as discovery tools for fostering reasonable representations of a collective will, sense-making, and agreement-seeking. At the same time, we caution against dangerously misguided uses, such as enabling binding decisions, fostering gradual disempowerment or post-rationalizing political outcomes.