The authors formalize a mechanism for collecting and aggregating public reports about deployed AI systems, aiming to surface unknown harms and enable accountability.
Democratic AI is Possible. The Democracy Levels Framework Shows How It Might Work
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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.
fields
cs.CY 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Aggregated Individual Reporting for Post-Deployment Evaluation
The authors formalize a mechanism for collecting and aggregating public reports about deployed AI systems, aiming to surface unknown harms and enable accountability.