Bibliography

• On the Mars prompt

  • A great exploration of the distinct challenges of governing Mars (as opposed to the more general topic I’m exploring of future governance systems) can be found in: Robinson, K. S. (1993, 1996, 1997). Red Mars/Green Mars/Blue Mars. Bantam Spectra.

  • Helene Landemore apparently uses a similar thought experiment in a class she teaches: Thornhill, J. (2021, 27 May). Designing democracy on Mars can improve how it works on Earth. The Financial Times.

  • Bruce Schneier wrote a piece a few weeks ago with a similar prompt: Schneier, B. (2023, 7 August). Re-imagining democracy for the 21st century, possibly without the trappings of the 18th century. The Conversation. https://theconversation.com/re-imagining-democracy-for-the-21st-century-possibly-without-the-trappings-of-the-18th-century-210586.

• On terra nullius

  • Terra nullius. (2023, 18 August). In Wikipedia. https://en.wikipedia.org/wiki/Terra_nullius

• On the historical evolution of institutional design:

  • Rockmore, D.N., Fang, C., Foti, N.J., Ginsburg, T. and Krakauer, D.C. (2018). The cultural evolution of national constitutions. Journal of the Association for Information Science and Technology, 69: 483-494. https://doi.org/10.1002/asi.23971.

  • Law, D. S., & Versteeg, M. (2012). The Declining Influence of the United States Constitution. New York University Law Review, 87(3).

• Material relevant to some of the future posts listed above

  • Privacy-preserving machine learning and governance: Bluemke, E., Collins, T., Garfinkel, B. and Trask, A. (2023, 25 March). Exploring the Relevance of Data Privacy-Enhancing Technologies for AI Governance Use Cases. ArXiv. https://arxiv.org/abs/2303.08956

  • One of many good primers on embedding vectors: Markowitz, D. (2022, 23 March). Meet AI’s multitool: Vector embeddings. Google Cloud Blog. https://cloud.google.com/blog/topics/developers-practitioners/meet-ais-multitool-vector-embeddings

  • Interpretability research and reflective equilibrium:

    • A good introduction to interpretability research: Olah, C. et al. (2018, 6 March). The Building Blocks of Interpretability. Distill. 10.23915/distill.00010. https://distill.pub/2018/building-blocks/

    • An overview of reflective equilibrium in general:

      • Daniels, N. (2020). Reflective Equilibrium. The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/archives/sum2020/entries/reflective-equilibrium/.

      • AI and reflective equilibrium: Duettman, A. (2023, 23 January). The Human-AI Reflective Equilibrium. LessWrong. https://www.lesswrong.com/posts/W7sEv69cQzW8D8SMr/the-human-ai-reflective-equilibrium

  • In terms of information-theoretic comparison of governance systems, I recently came across this, which is the closest thing I’ve found to the lens I want to apply: Shalizi, C. (2023, 17 July). Democracy. http://bactra.org/notebooks/democracy.html.

• On optimizing for imperfect measures:

  • A great source overviewing the challenges of this with respect to AI can be found in: Christian, B. (2020). The Alignment Problem: Machine Learning and Human Values. W. W. Norton & Company.

  • For a more condensed and general treatment overview, see: Goodhart’s Law. (2023, 13 August). In Wikipedia. https://en.wikipedia.org/wiki/Goodhart%27s_law 

• Scalable nuance

  • Go: Silver, D., Huang, A., Maddison, C. et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489. https://doi.org/10.1038/nature16961; Silver, D., Schrittwieser, J., Simonyan, K. et al. (2017). Mastering the game of Go without human knowledge. Nature 550, 354–359. https://doi.org/10.1038/nature24270; Silver, D., Hubert, T.,  Schrittwieser, J. et al. (2018, 7 December) A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science 362, 1140-1144. 10.1126/science.aar6404.

  • Faces: Guo, G., Zhang, N. (2019). A survey on deep learning based face recognition. Computer Vision and Image Understanding 189. https://doi.org/10.1016/j.cviu.2019.102805.

  • On the high modernist state and “legibility,” much of James C. Scott’s work is relevant. The classic is: Scott, J.C. (1999, 8 February). Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed. Yale University Press.

• Lossy compression

  • For an overview, see: Lossy compression. (2023, 9 August). In Wikipedia. https://en.wikipedia.org/wiki/Lossy_compression.

  • Inspiration for the diagram I used comes from auto-encoders: Autoencoder. (2023, 14 August). In Wikipedia. https://en.wikipedia.org/wiki/Autoencoder.

I’m sure there’s relevant stuff I haven’t found – please send suggestions (see disclaimer in main piece)