The Democratic Inputs to AI grant program funded 10 teams to develop and test their ideas for processes to help govern AI. We summarized some of their findings in our recap blog post, and here we present a repository where they have shared their code, alongside links to their reports and contact information.
If you would like to see the teams describe their ambitions during the September 2023 OpenAI Demo Day, please watch the recording here.
- Case Law for AI Policy
- Collective Dialogues for Democratic Policy Development
- Deliberation at Scale: Socially democratic inputs to AI
- Democratic Fine-Tuning
- Aligned: An Alignment Platform
- Generative Social Choice
- Inclusive.AI: Engaging Underserved Populations in Democratic Decision-Making on AI
- Making AI Transparent and Accountable by Rappler
- Ubuntu-AI: A Platform for Equitable and Inclusive Model Training
- vTaiwan and Chatham House: Bridging the Recursive Public
Description: Creating a robust case repository around AI interaction scenarios that can be used to make case-law-inspired judgments through a process that democratically engages experts, laypeople, and key stakeholders.
Team members:
- Quan Ze (Jim) Chen
- Kevin Feng
- Inyoung Cheong
- Amy X. Zhang
- King Xia
Description: Developing policies that reflect informed public will using collective dialogues to efficiently scale democratic deliberation and find areas of consensus.
Team members:
- Andrew Konya
- Lisa Schirch
- Colin Irwin
Description: Enabling democratic deliberation in small group conversations conducted via AI-facilitated video calls.
Team members:
- Jorim Theuns
- Evelien Nieuwenburg
- Pepijn Verburg
- Lei Nelissen
- Brett Hennig
- Rich Rippin
- Ran Haase
- Aldo de Moor
- CeesJan Mol
- Naomi Esther
- Rolf Kleef
- Bram Delisse
Description: Eliciting values from participants in a chat dialogue in order to create a moral graph of values that can be used to fine-tune models.
LINKS: Report; Website; Contact
Team members:
- Joe Edelman
- Oliver Klingefjord
- Ivan Vendrov
Description: Developing guidelines for aligning AI models with live, large-scale participation and a 'community notes' algorithm.
Team members:
- Ethan Shaotran
- Ido Pesok
- Sam Jones
Description: Distilling a large number of free-text opinions into a concise slate that guarantees fair representation using mathematical arguments from social choice theory.
Team members:
- Sara Fish
- Paul Gölz
- Ariel Procaccia
- Gili Rusak
- Itai Shapira
- Manuel Wüthrich
Description: Facilitating decision-making processes related to AI using a platform with decentralized governance (e.g., DAO) mechanisms that empower underserved groups.
Team members:
- Yang Wang
- Yun Huang
- Tanusree Sharma
- Dawn Song
- Sunny Liu
- Jeff Hancock
Description: Enabling discussion and understanding of participants' views on complex, polarizing topics via linked offline and online processes.
Team members:
- Gemma B. Mendoza
- Gilian Uy
- Don Kevin Hapal
- Ogoy San Juan
- Maria Ressa
Description: Returning value to those who help create it while facilitating LLM development and ensuring more inclusive knowledge of African creative work.
Team members:
- Ron Eglash
- Joshua Mounsey
- Micheal Nayebare
- Rehema Baguma
- Ussen Kimanuka
Description: Using an adapted vTaiwan methodology to create a recursive, connected participatory process for AI.
LINKS: Report; Website; Contact
Team members:
- Alex Krasodomski-Jones
- Carl Miller
- Flynn Devine
- Jia-Wei (Peter) Cui
- Shu Yang Lin