2026-05-13 ペンシルベニア州立大学（Penn State）
A Penn State-led research team tested different ways of sharing decision-making power, seeking a fairer, democratic method to give more people an equal opportunity to shape how AI behaves. Credit: Juan D. Villa Romero. All Rights Reserved.
＜関連情報＞
- https://www.psu.edu/news/information-sciences-and-technology/story/democratic-approach-challenge-improve-ai-tools
- https://www.nature.com/articles/s41598-026-40180-8
DAOベースの審議と投票による民主的ガバナンス、AIモデルにおける包括的な意思決定 Democratic governance through DAO-based deliberation and voting for inclusive decision making in AI models
Tanusree Sharma,Yujin Potter,Jongwon Park,Yiren Liu,Yun Huang,Sunny Liu,Dawn Song,Jeff Hancock & Yang Wang
Scientific Reports Published:03 March 2026
DOI:https://doi.org/10.1038/s41598-026-40180-8
Abstract
A major criticism of AI development is the lack of transparency, such as, inadequate documentation and traceability in its design and decision-making processes, leading to adverse outcomes including discrimination, lack of inclusivity and representation, and breaches of legal regulations. Underserved populations, in particular, are disproportionately affected by these design decisions. Furthermore, traditional social science techniques such as interviews, focus groups, and surveys struggle to adequately capture user needs and expectations in the digital era, due to their inherent limitations in deliberation, consensus-building, and providing consistent insights. We developed a democratic decision framework utilizing Decentralized Autonomous Organization (DAO) to enable underserved groups to deliberate and reach a consensus on key AI issues. To assess our proposed democratic decision mechanism, we conducted a case study on updating AI model specification based on diverse stakeholders input. We focus on reducing stereotypical biases in text-to-image systems, particularly gender bias in image generation from text prompts. We designed and experimented various governance configurations, including decision aggregation schemes and decision power, to examine how democratic processes could guide updates to AI model. Through a 2 × 2 experimental design, we tested various aggregation schemes (ranked vs. quadratic) and decision power distribution (equal vs. 20/80 differential) in a randomized online experiment (n=177) with participants from the global south and people with disabilities, to study how the varying governance mechanisms impact people’s perceptions of the decision-making processes and resulting output of the AI Model specification. Our results indicate that despite their diverse backgrounds, participants showed convergence in deliberations on several aspects, including user control over image generation, multiple output options for user selection, and the social appropriateness and accuracy of generated images. Our study underscores the importance of use of appropriate governance in democratic decision-making in AI alignment. Notably, the combination of quadratic preference aggregation method which gives minorities more voice and equal decision power distribution, was perceived as a fairer and democratic approach.
