Position: Democratic AI is Possible. The Democracy Levels Framework Shows How It Might Work.
Aviv Ovadya, Kyle Redman, Luke Thorburn, Quan Ze Chen, Oliver Smith, Flynn Devine, Andrew Konya, Smitha Milli
Abstract
This position paper argues that effectively "democratizing AI" requires democratic governance and alignment of AI, and that this is particularly valuable for decisions with systemic societal impacts. Initial steps—such as Meta's *Community Forums* and Anthropic's *Collective Constitutional AI*—have illustrated a promising direction, where democratic processes could be used to meaningfully improve public involvement and trust in critical decisions. To more concretely explore what increasingly democratic AI might look like, we provide a "Democracy Levels" framework and associated tools that: (i) define milestones toward meaningfully democratic AI—which is also crucial for substantively pluralistic, human-centered, participatory, and public-interest AI, (ii) can help guide organizations seeking to increase the legitimacy of their decisions on difficult AI governance and alignment questions, and (iii) support the evaluation of such efforts.
BibTeX
@inproceedings{
ovadya2025position,
title={Position: Democratic {AI} is Possible. The Democracy Levels Framework Shows How It Might Work.},
author={Aviv Ovadya and Kyle Redman and Luke Thorburn and Quan Ze Chen and Oliver Smith and Flynn Devine and Andrew Konya and Smitha Milli and Manon Revel and Kevin Feng and Amy X Zhang and Bilva Chandra and Michiel A. Bakker and Atoosa Kasirzadeh},
booktitle={Forty-second International Conference on Machine Learning Position Paper Track},
year={2025},
url={https://openreview.net/forum?id=yYJo8czj4f}
}