Academic Readings
Brown, L. (2025). Artificial Intelligence & Trusts and Trustees: A new dawn of investment opportunities and risks? Trusts & Trustees, 31(5), 210–215. PDF: https://academic.oup.com/tandt/article/31/5/210/7904689
Costanza-Chock, S., Harvey, E., Raji, I. D., Czernuszenko, M., & Buolamwini, J. (2023). Who Audits the Auditors? Recommendations from a Field Scan of the Algorithmic Auditing Ecosystem. arXiv. PDF: https://arxiv.org/abs/2310.02521
Benthall, S., & Shekman, D. (2023). Designing Fiduciary Artificial Intelligence. arXiv. PDF: https://arxiv.org/abs/2308.02435
Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., & Fritz, M. (2023, May 5). Not what you’ve signed up for: Compromising real-world LLM-integrated applications with indirect prompt injection. arXiv. URL: https://arxiv.org/abs/2302.12173
Sung Park, J., O’Brien, J. C., Cai, C. J., Ringel Morris, M., Liang, P., & Bernstein, M. S. (2023, April 7). Generative Agents: Interactive Simulacra of Human Behavior. arXiv. URL: https://arxiv.org/abs/2304.03442
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023, May 17). Tree of Thoughts: Deliberate Problem Solving with Large Language Models. arXiv. URL: https://arxiv.org/abs/2305.10601
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023, March 10). ReAct: Synergizing Reasoning and Acting in Language Models. arXiv. URL: https://arxiv.org/abs/2210.03629
Noy, S., & Zhang, W. (2023, March 10). Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence. MIT Economics. URL: https://economics.mit.edu/sites/default/files/inline-files/Noy_Zhang_GenerativeAI.pdf
Brynjolfsson, E., Li, D., & Raymond, L. (2023). Generative AI at Work. NBER. URL: https://www.nber.org/papers/w31161
Dell’Acqua, F., McFowland, E., Mollick, E., Lifshitz Assaf, H., Kellogg, K., Rajendra, S., Krayer, L., Candelon, F., & Lakhani, K. (2023). Navigating the Jagged Technological Frontier. Harvard Business School. URL: https://www.hbs.edu/ris/Publication%20Files/23-101_3aa1b6d1-8a6b-4f1d-807f-4e2a88ac57f2.pdf
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? In FAccT ’21 (pp. 610–623). ACM. PDF: https://dl.acm.org/doi/pdf/10.1145/3442188.3445922
Pascale, R., Milleman, M., & Gioja, L. (2021). Surfing the Edge of Chaos: The Laws of Nature and the New Laws of Business. Currency.
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing. arXiv. PDF: https://arxiv.org/abs/2001.00973
2019 and earlier
DeVries, T., Misra, I., Wang, C., & van der Maaten, L. (2019, June 18). Does Object Recognition Work for Everyone? arXiv. PDF: https://arxiv.org/pdf/1906.02659
Möslein, F. (2018). Robots in the Boardroom: Artificial Intelligence and Corporate Law. In W. Barfield & U. Pagallo (Eds.), Research Handbook on the Law of Artificial Intelligence, pp. 649–635. Edward Elgar Publishing. PDF: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3037403 irep.ntu.ac.uk+10
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017, December 6). Attention Is All You Need. arXiv. URL: https://arxiv.org/abs/1706.03762
Forsgren, N., Humble, J., & Kim, G. (2018). Accelerate: The Science of Lean Software and DevOps. IT Revolution Press. URL: https://itrevolution.com/book/accelerate/
Hodges, A. (2014). Alan Turing: The Enigma. Princeton University Press.
Kurzweil, R. (2006). The Singularity Is Near. Penguin Books.
Hawkins, J., Rudnicki, S., & Blakeslee, S. (2005). On Intelligence. Audible Studios.
Hofstadter, D. R. (1999). Gödel, Escher, Bach: An Eternal Golden Braid. Basic Books.
Minsky, M. (1988). The Society of Mind. Simon & Schuster.
Marquet, L. D. (2013). Turn the Ship Around! Portfolio.
Clippinger, J. H., III. (1999). The Biology of Business. Jossey-Bass.