AI can help people talk and decide better in large groups
AI Should Facilitate Democratic Deliberation at Scale
Human-Computer InteractionArtificial IntelligenceComputation and LanguageComputers and Society
Summary
Making decisions together in big groups can be hard because people face many obstacles like misunderstandings or unfair attention. The authors argue that AI tools can help by making it easier for everyone to join in and share their views respectfully without replacing human choices. They suggest that these AI systems should respect people's freedom, encourage fairness, and support active participation. They also warn about problems like AI bias and overtrust to watch out for. Finally, they encourage AI builders to focus on tools that really improve thoughtful and honest discussions, not just increase clicks or comments.
What this means in practice
- •For online platform designers: Create AI features that encourage respectful and equal participation in large digital discussion forums to improve quality of democratic debate.
- •For community organizers: Use AI tools that lower barriers for diverse voices and help manage large-scale group discussions without taking over decision-making.
A position paper. It proposes an approach and reports no results.
Authors
José Ramón Enríquez, Jiaxin Pei, Alex Pentland
Abstract
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.