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, Manon Revel, Kevin Feng, Amy X Zhang, Bilva Chandra, Michiel A. Bakker, Atoosa Kasirzadeh
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:81930-81961, 2025.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-ovadya25a, title = {Position: Democratic {AI} is Possible. The Democracy Levels Framework Shows How It Might Work.}, author = {Ovadya, Aviv and Redman, Kyle and Thorburn, Luke and Chen, Quan Ze and Smith, Oliver and Devine, Flynn and Konya, Andrew and Milli, Smitha and Revel, Manon and Feng, Kevin and Zhang, Amy X and Chandra, Bilva and Bakker, Michiel A. and Kasirzadeh, Atoosa}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {81930--81961}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/ovadya25a/ovadya25a.pdf}, url = {https://proceedings.mlr.press/v267/ovadya25a.html}, 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.} }
Endnote
%0 Conference Paper %T Position: Democratic AI is Possible. The Democracy Levels Framework Shows How It Might Work. %A Aviv Ovadya %A Kyle Redman %A Luke Thorburn %A Quan Ze Chen %A Oliver Smith %A Flynn Devine %A Andrew Konya %A Smitha Milli %A Manon Revel %A Kevin Feng %A Amy X Zhang %A Bilva Chandra %A Michiel A. Bakker %A Atoosa Kasirzadeh %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-ovadya25a %I PMLR %P 81930--81961 %U https://proceedings.mlr.press/v267/ovadya25a.html %V 267 %X 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.
APA
Ovadya, A., Redman, K., Thorburn, L., Chen, Q.Z., Smith, O., Devine, F., Konya, A., Milli, S., Revel, M., Feng, K., Zhang, A.X., Chandra, B., Bakker, M.A. & Kasirzadeh, A.. (2025). Position: Democratic AI is Possible. The Democracy Levels Framework Shows How It Might Work.. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:81930-81961 Available from https://proceedings.mlr.press/v267/ovadya25a.html.

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