# AI Should Facilitate Democratic Deliberation at Scale

arXiv: https://arxiv.org/abs/2609.20059 · HTML: https://mlsanity.com/paper/2609.20059/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 120 references that are in the map.
- First posted on arXiv 17 Sep 2026.

## Builds on (most cited first, 25 of 25)

- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [A New Generation of Perspective API: Efficient Multilingual Character-level Transformers](https://mlsanity.com/paper/2202.11176/) (2022)
- [Cultural Bias and Cultural Alignment of Large Language Models](https://mlsanity.com/paper/2311.14096/) (2023)
- [The Ethics of Advanced AI Assistants](https://mlsanity.com/paper/2404.16244/) (2024)
- [Toxicity Detection: Does Context Really Matter?](https://mlsanity.com/paper/2006.00998/) (2020)
- [How Well Do LLMs Represent Values Across Cultures? Empirical Analysis of LLM Responses Based on Hofstede Cultural Dimensions](https://mlsanity.com/paper/2406.14805/) (2024)
- [LLMs Beyond English: Scaling the Multilingual Capability of LLMs with Cross-Lingual Feedback](https://mlsanity.com/paper/2406.01771/) (2024)
- [Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts](https://mlsanity.com/paper/2306.11372/) (2023)
- [Opportunities and Risks of LLMs for Scalable Deliberation with Polis](https://mlsanity.com/paper/2306.11932/) (2023)
- [Densing Law of LLMs](https://mlsanity.com/paper/2412.04315/) (2024)
- [Algorithmic Amplification of Politics on Twitter](https://mlsanity.com/paper/2110.11010/) (2021)
- [The Homogenizing Effect of Large Language Models on Human Expression and Thought](https://mlsanity.com/paper/2508.01491/) (2025)
- [Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions](https://mlsanity.com/paper/1702.01119/) (2017)
- [Political-LLM: Large Language Models in Political Science](https://mlsanity.com/paper/2412.06864/) (2024)
- [Can LLMs Recognize Toxicity? A Structured Investigation Framework and Toxicity Metric](https://mlsanity.com/paper/2402.06900/) (2024)
- [Democratic Policy Development using Collective Dialogues and AI](https://mlsanity.com/paper/2311.02242/) (2023)
- ['Generative CI' through Collective Response Systems](https://mlsanity.com/paper/2302.00672/) (2023)
- [Reconsidering Tweets: Intervening During Tweet Creation Decreases Offensive Content](https://mlsanity.com/paper/2112.00773/) (2021)
- [WAXAL: A Large-Scale Multilingual African Language Speech Corpus](https://mlsanity.com/paper/2602.02734/) (2026)
- [The Basic B*** Effect: The Use of LLM-based Agents Reduces the Distinctiveness and Diversity of People's Choices](https://mlsanity.com/paper/2509.02910/) (2025)
- [Can AI Truly Represent Your Voice in Deliberations? A Comprehensive Study of Large-Scale Opinion Aggregation with LLMs](https://mlsanity.com/paper/2510.05154/) (2025)
- [Quantifying attention via dwell time and engagement in a social media browsing environment](https://mlsanity.com/paper/2209.10464/) (2022)
- [Supporting Online Discussions: Integrating AI Into the adhocracy+ Participation Platform To Enhance Deliberation](https://mlsanity.com/paper/2409.07780/) (2024)
- [Impact of AI Tools on Learning Outcomes: Decreasing Knowledge and Over-Reliance](https://mlsanity.com/paper/2510.16019/) (2025)

## Abstract (arXiv)

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.