Bringing AI Participation Down to Scale: A Comment on Open AIs Democratic Inputs to AI Project
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Abstract (arXiv)
In 2023, Open AIs Democratic Inputs program funded 10 teams to design procedures for public participation in generative AI. In this Perspective, we review the results of the project, drawing on interviews with some of the teams and our own experiences conducting participation exercises, we identify several shared yet largely unspoken assumptions of the Democratic Inputs program 1. that participation must be scalable 2. that the object of participation is a single model 3. that there must be a single form of participation 4. that the goal is to extract abstract principles 5. that these principles should have consensus 6. that publics should be representative and encourage alternative forms of participation in AI, perhaps not undertaken by tech companies.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 48 references that are in the map. First posted on arXiv 16 Jul 2024.
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13 of 48 references.
References · 13
- 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
- 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 911
- 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
- 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 683
- 2023Don't Make Your LLM an Evaluation Benchmark Cheatercited by 194
- 2021Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasetscited by 179
- 2020Participation is not a Design Fix for Machine Learningcited by 110
- 2024What are human values, and how do we align AI to them?cited by 32
- 2024Participation in the age of foundation modelscited by 32
- 2020Against Scale: Provocations and Resistances to Scale Thinkingcited by 18
- 2023Democratic Policy Development using Collective Dialogues and AIcited by 16
- 2023Case Repositories: Towards Case-Based Reasoning for AI Alignmentcited by 11
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