Can AI mediation improve democratic deliberation?
Not on the map. Why?
Abstract (arXiv)
The strength of democracy lies in the free and equal exchange of diverse viewpoints. Living up to this ideal at scale faces inherent tensions: broad participation, meaningful deliberation, and political equality often trade off with one another (Fishkin, 2011). We ask whether and how artificial intelligence (AI) could help navigate this "trilemma" by engaging with a recent example of a large language model (LLM)-based system designed to help people with diverse viewpoints find common ground (Tessler, Bakker, et al., 2024). Here, we explore the implications of the introduction of LLMs into deliberation augmentation tools, examining their potential to enhance participation through scalability, improve political equality via fair mediation, and foster meaningful deliberation by, for example, surfacing trustworthy information. We also point to key challenges that remain. Ultimately, a range of empirical, technical, and theoretical advancements are needed to fully realize the promise of AI-mediated deliberation for enhancing citizen engagement and strengthening democratic deliberation.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 81 references that are in the map. First posted on arXiv 9 Jan 2026.
Builds on
29 of 81 references.
References · 29
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,224
- 2016Concrete Problems in AI Safetycited by 1,642
- 2020Artificial Intelligence, Values and Alignmentcited by 448
- 2023Towards Measuring the Representation of Subjective Global Opinions in Language Modelscited by 437
- 2018AI safety via debatecited by 400
- 2022Self-critiquing models for assisting human evaluatorscited by 380
- 2021Recursively Summarizing Books with Human Feedbackcited by 290
- 2022Measuring Progress on Scalable Oversight for Large Language Modelscited by 225
- 2022Fine-tuning language models to find agreement among humans with diverse preferencescited by 189
- 2022Power to the People? Opportunities and Challenges for Participatory AIcited by 138
- 2024Collective Constitutional AI: Aligning a Language Model with Public Inputcited by 123
17 more
- 2024The Ethics of Advanced AI Assistantscited by 107
- 2024STaR-GATE: Teaching Language Models to Ask Clarifying Questionscited by 104
- 2023Scalable AI Safety via Doubly-Efficient Debatecited by 57
- 2024Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discriminationcited by 56
- 2023Generative Social Choicecited by 43
- 2023Debate Helps Supervise Unreliable Expertscited by 43
- 2023Opportunities and Risks of LLMs for Scalable Deliberation with Poliscited by 42
- 2024Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Makingcited by 38
- 2024Supernotes: Driving Consensus in Crowd-Sourced Fact-Checkingcited by 22
- 2023Democratic Policy Development using Collective Dialogues and AIcited by 17
- 2025Language Agents as Digital Representatives in Collective Decision-Makingcited by 16
- 2024AI and the Future of Digital Public Squarescited by 6
- 2023Distortion Under Public-Spirited Votingcited by 4
- 2014A Statistical Decision-Theoretic Framework for Social Choicecited by 3
- 2024Learning to Manipulate under Limited Informationcited by 3
- 2025Human-AI Complementarity: A Goal for Amplified Oversightcited by 3
- 2024Can LLMs advance democratic values?cited by 2
Built on by
Not cited yet.