# Bringing AI Participation Down to Scale: A Comment on Open AIs Democratic Inputs to AI Project

arXiv: https://arxiv.org/abs/2407.11613 · HTML: https://mlsanity.com/paper/2407.11613/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Builds on (most cited first, 13 of 13)

- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [BBQ: A Hand-Built Bias Benchmark for Question Answering](https://mlsanity.com/paper/2110.08193/) (2021)
- [Don't Make Your LLM an Evaluation Benchmark Cheater](https://mlsanity.com/paper/2311.01964/) (2023)
- [Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets](https://mlsanity.com/paper/2106.10328/) (2021)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [What are human values, and how do we align AI to them?](https://mlsanity.com/paper/2404.10636/) (2024)
- [Participation in the age of foundation models](https://mlsanity.com/paper/2405.19479/) (2024)
- [Against Scale: Provocations and Resistances to Scale Thinking](https://mlsanity.com/paper/2010.08850/) (2020)
- [Democratic Policy Development using Collective Dialogues and AI](https://mlsanity.com/paper/2311.02242/) (2023)
- [Case Repositories: Towards Case-Based Reasoning for AI Alignment](https://mlsanity.com/paper/2311.10934/) (2023)
- [Case Law Grounding: Using Precedents to Align Decision-Making for Humans and AI](https://mlsanity.com/paper/2310.07019/) (2023)

## Built on by (most cited first, 1 of 1)

- [Editorial Alignment: A Participatory Approach to Engaging Editorial Expertise in LLM-mediated Knowledge Dissemination](https://mlsanity.com/paper/2606.20258/) (2026)

## 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.