Required Readings

Week 1

Bowles, C. (2020, November 26). All These Worlds Are Yours. Cennydd Bowles. https://cennydd.com/blog/all-these-worlds-are-yours
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

Crawford, K., & Paglen, T. (2019, September 19). Excavating AI: The Politics of Images in Machine Learning Training Sets. Excavating AI. https://excavating.ai
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

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
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

Alfrink, K., Keller, I., Kortuem, G., & Doorn, N. (2022). Contestable AI by Design: Towards a Framework. Minds and Machines, 33(4), 613–639. https://doi.org/10/gqnjcs
Gilbert, T. K., Lambert, N., Dean, S., Zick, T., Snoswell, A., & Mehta, S. (2023). Reward Reports for Reinforcement Learning. Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 84–130. https://doi.org/10/gs9cnh

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
Birhane, A., Isaac, W., Prabhakaran, V., Diaz, M., Elish, M. C., Gabriel, I., & Mohamed, S. (2022). Power to the People? Opportunities and Challenges for Participatory AI. Equity and Access in Algorithms, Mechanisms, and Optimization, 1–8. https://doi.org/10/grnj99

Alfrink, K., Keller, I., Yurrita Semperena, M., Bulygin, D., Kortuem, G., & Doorn, N. (2024). Envisioning Contestability Loops: Evaluating the Agonistic Arena as a Generative Metaphor for Public AI. She Ji: The Journal of Design, Economics, and Innovation, 10(1), 53–93. https://doi.org/10/gtzwft
Capel, T., & Brereton, M. (2023). What is Human-Centered about Human-Centered AI? A Map of the Research Landscape. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–23. https://doi.org/10/gr6q26