Aligning AI with Public Values: Deliberation and Decision-Making for Multimodal LLM Governance

ACL ARR 2025 February Submission4808 Authors

Abstract: How AI models should deal with political topics has been discussed, but it remains challenging and requires better governance. This paper examines the governance of large language models through individual and collective deliberation, focusing on politically sensitive videos. We conducted a two-step study: interviews with 10 journalists established a baseline understanding of expert video interpretation; 114 individuals through deliberation using Inclusive.AI, a platform that facilitates democratic decision-making through decentralized autonomous organization (DAO) mechanisms. Our findings reveal distinct differences in interpretative priorities: while experts emphasized emotion and narrative, the general public prioritized factual clarity, objectivity, and emotional neutrality. Furthermore, we examined how different governance mechanisms: quadratic vs. weighted voting and equal vs. 20/80 voting power shape users' decision-making regarding AI behavior. Results indicate that voting methods significantly influence outcomes, with quadratic voting reinforcing perceptions of liberal democracy and political equality. Our study underscores the necessity of selecting appropriate governance mechanisms to better capture user perspectives and suggests decentralized AI governance as a potential way to facilitate broader public engagement in AI development, ensuring that varied perspectives meaningfully inform design decisions.

Paper Type: Long

Research Area: Ethics, Bias, and Fairness

Research Area Keywords: AI Governance, Fairness, Transparency, DAO, Decentralized Autonomous Organizations

Contribution Types: Publicly available software and/or pre-trained models, Data analysis

Languages Studied: English

Submission Number: 4808