Translational Research and Perspectives

Online discourse faces challenges in facilitating substantive and productive political conversations. Recent technologies have explored the potential of generative AI to promote civil discourse, encourage the development of mutual understanding in a discussion, produce feedback that enables people to converge in their views, and provide usable citizen input on policy questions posed to the public by governments and civil society. In this paper, we present a framework to help policymakers, technologists, and the public assess potential opportunities and risks when incorporating generative AI into online platforms for discussion and deliberation in order to strengthen democratic practices and help democratic governments make more effective and responsive policy decisions.

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.

Existing venues for public engagement, from town halls to online comment threads, fail to foster thoughtful discussion about complex policy issues. Attracting only a selection of voices, these models constrain not only whose preferences are heard, but which solutions can even be imagined.

Generative AI offers an alternative by reducing participation costs while optimizing for inclusivity, engagement, and decision-making. When purposefully designed, AI can facilitate the kind of structured, reason-giving dialogue that helps diverse communities identify shared priorities and workable policy trade-offs. Yet the design space remains largely unexplored—most platforms operate on heuristics rather than evidence, and we lack systematic knowledge of what promotes civil discourse, broad participation, and actionable outcomes at scale.

Our research provides rigorous, empirical evidence on how to foster meaningful civic participation in the digital age. We study the mechanisms that enable diverse publics to articulate nuanced preferences, engage with opposing views, and co-produce implementable policy—while preserving agency, augmenting citizenship, and amplifying underrepresented voices.

This is an opinion piece on how large language models (LLMs) are making complex data analysis available to everyone, and the risks involved.