This page will feature background material that conference participants are encouraged to explore ahead of the conference. Conference participants are encouraged to focus on the resources in areas that they are least familiar with.
If you are attending the conference and would like us to feature a resource on this page, please contact Matthew Meyers (matthew.meyers@yale.edu)
The following resources are designed to be an introduction to Citizens’ Assemblies, mini-publics, and theories about collective intelligence through deliberation among ordinary people.
Citizens’ Assemblies
- Documentary on the French Citizens’ Convention on Climate (turn on the Youtube subtitles and you can watch it with English subtitles)
- Mark Warren and Hilary Pearse. 2008. Designing Deliberative Democracy: the British Columbia Citizens’ Assembly, Cambridge University Press.
- Farrell, David M., and Jane Suiter. 2019. Reimagining Democracy: Lessons in Deliberative Democracy from the Irish Front Lines. Ithaca, NY: Cornell University Press.
- “’Co-construction’ in Deliberative Democracy: Lessons from the French Citizens’ Convention for Climate.” With Louis-Gaëtan Giraudet, Bénédicte Apouey, Hazem Arab, Simon Baeckelandt, Philippe Begout, et al. Humanities & Social Sciences Communications 9: 207, 2022.
- Min Reuchamp et al. 2023. De Gruyter Handbook of Citizens’ Assemblies.
- 2020. Innovative Citizen Participation and New Democratic Institutions: Catching the Deliberative Wave. [documents 800 lot-based deliberative assemblies]
Deliberative Polling
- James Fishkin. 2018. Deliberation when the People are Thinking: Revitalizing Our Politics Through Public Deliberation. Oxford: Oxford University Press.
- Aviv Ovaya, “Deliberative Polls, Citizen Assemblies, and an Online Deliberation Platform”
Yale Lottery Conference organized by Ali Cirone Dec. 2023:
More general political theory debates around democracy and the value and role of lot-base assemblies in it
- Alex Guerrero. 2014.“Against Elections? The Lottocratic Alternative” Philosophy and PublicAffairs,
- John Gastil and Erik Olin Wright (eds). 2018. Legislature by Lot, Verso.
- Lafont, Christina. 2014. “Deliberation, Participation, and Democratic Legitimacy: Should Deliberative Mini-Publics Shape Public Policy?” Journal of Political Philosophy
Absolute classic on representative government and the history of lot-based democracy
- Bernard Manin. 1997. The Principles of Representative Government. Chicago: Chicago University Press
The following resources are designed to be an introduction to how modern AI models work and some of the key AI policy considerations
AI technical knowledge essentials:
- First three videos of the Neural Networks playlist, by 3blue1brown (Grant Sanderson). About 1 hour total.
- How Deep Learning Works, bySamuel K. Moore et al.
- MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention, by Ava Amini. Until minute 48
- The Unreasonable Effectiveness of Recurrent Neural Networks, by Andrej Karpathy.
- Intro to Large Language Models Lecture by Andrej Karpathy of OpenAI
- Watch an A.I. Learn to Write by Reading Nothing but … by Aatish Batia, NYT
Additional materials for AI technical knowledge
- “A critical review of recurrent neural networks for sequence learning” by Zachary C. Lipton, John Berkowitz, and Charles Elkan.
- “Understanding LSTM Networks” by Christopher Olah.
- “Understanding GRU Networks” by Simeon Kostadinov.
- “Illustrated guide to LSTMs and GRUs”, by Michael Phi.
- “Explanation of Transformers” – Transformers are the state of the art form of sequence to sequence models and are the core of GPT (the T stands for transformer).
- “Attention is All You Need” by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin.
- “The Illustrated Transformer”, by Jay Alammar. (visual version of “Attention is All You Need”)
- “Improving Language Understanding by Generative Pre-Training” by Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. This is the paper that introduced the term “GPT”
- “Language Models are Few-Shot Learners” by OpenAI.
- “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding” by Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova.
AI Governance and Policy
- “We need to democratize AI”, by Hélène Landemore and John Tasioulas,
- “What the EU’s tough AI law means for research and ChatGPT” by Elizabeth Gibney
- Biden Executive Order Official Fact Sheet
- Artificial Intelligence Policy: A Primer and Roadmap by Ryan Calo