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AI & Society

Required Readings

In alphabetical order. Refer to course roadmap and assignments for order of reading and reflection questions.

  1. Alfrink, K., Keller, I., Kortuem, G., & Doorn, N. (2021). Contestable AI by Design: Towards A Framework. Manuscript Submitted for Publication.
  2. Alkhatib, A., & Bernstein, M. (2019). Street–level algorithms: A theory at the gaps between policy and decisions. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10/gf9h69
  3. Almada, M. (2019). Human intervention in automated decision-making: Toward the construction of contestable systems. Proceedings of the 17th International Conference on Artificial Intelligence and Law, ICAIL 2019, 2–11. https://doi.org/10/gghft8
  4. Bowles, C. (2020, November 26). All These Worlds Are Yours. Cennydd Bowles. https://cennydd.com/blog/all-these-worlds-are-yours
  5. Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data and Society, 3(1), 1–12. https://doi.org/10/gcd3mk
  6. Cheng, H.-F., Wang, R., Zhang, Z., O’Connell, F., Gray, T., Harper, F. M., & Zhu, H. (2019). Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Paper 559. https://doi.org/10.1145/3290605.3300789
  7. Crawford, K., & Paglen, T. (2019, September 19). Excavating AI: The Politics of Images in Machine Learning Training Sets. Excavating AI. https://excavating.ai
  8. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2020). Datasheets for Datasets. ArXiv:1803.09010 [Cs]. http://arxiv.org/abs/1803.09010
  9. Hill, D. (2019, February 2). The city is my homescreen. Dark Matter & Trojan Horses. https://medium.com/dark-matter-and-trojan-horses/the-city-is-my-homescreen-317673e0f57a
  10. Katell, M., Young, M., Dailey, D., Herman, B., Guetler, V., Tam, A., Bintz, C., Raz, D., & Krafft, P. M. (2020). Toward situated interventions for algorithmic equity: Lessons from the field. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 45–55. https://doi.org/10.1145/3351095.3372874
  11. König, P. D., & Wenzelburger, G. (2021). The legitimacy gap of algorithmic decision-making in the public sector: Why it arises and how to address it. Technology in Society, 67, 101688. https://doi.org/10/gpk2ps
  12. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model Cards for Model Reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, 220–229. https://doi.org/10/gftgjg