From Experts to the Public: Governing Multimodal Language Models in Politically Sensitive Video Analysis
<|reference_start|>From Experts to the Public: Governing Multimodal Language Models in Politically Sensitive Video Analysis: This paper examines the governance of multimodal large language models (MM-LLMs) through individual and collective deliberation, focusing on analyses of politically sensitive videos. We conducted a two-step study: first, interviews with 10 journalists established a baseline understanding of expert video interpretation; second, 114 individuals from the general public engaged in deliberation using Inclusive.AI, a platform that facilitates democratic decision-making through decentralized autonomous organization (DAO) mechanisms. Our findings show that while experts emphasized emotion and narrative, the general public prioritized factual clarity, objectivity of the situation, and emotional neutrality. Additionally, we explored the impact of different governance mechanisms: quadratic vs. weighted ranking voting and equal vs. 20-80 power distributions on users decision-making on how AI should behave. Specifically, quadratic voting enhanced perceptions of liberal democracy and political equality, and participants who were more optimistic about AI perceived the voting process to have a higher level of participatory democracy. Our results suggest the potential of applying DAO mechanisms to help democratize AI governance.<|reference_end|>
@article{sharma2024from,
    title={From Experts to the Public: Governing Multimodal Language Models in
  Politically Sensitive Video Analysis},
    author={Tanusree Sharma, Yujin Potter, Zachary Kilhoffer, Yun Huang, Dawn
  Song, Yang Wang},
    journal={arXiv preprint arXiv:2410.01817},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01817},
    primaryClass={cs.CV cs.AI cs.CY}
}