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MAIS 624 (Rev. 1): Critical Approaches to Technology and Society Report a Broken Link

Unit 1—Introduction: A Critical Approach to Computational Cultures

Ali, S. M. (2016). A brief introduction to decolonial computing. XRDS: Crossroads, The ACM Magazine for Students, 22(4), 16–21.

Unit 2—The Datafication of Identity Politics: Subject Formation in a Digital Society

Deleuze, G. (1992). Postscript on the societies of control. October, 59(Winter), 3–7.
Goriunova, O. (2019). The digital subject: People as data as persons. Theory, Culture & Society, 36(6), 125–145.
Sanders, R. (2017). Self-tracking in the digital era: Biopower, patriarchy, and the new biometric body projects. Body & Society, 23(1), 36–63.

Unit 3—Surveillance & Prediction

Office of the Privacy Commissioner of Canada. (2012). The age of predictive analytics: From patterns to predictions. Government of Canada.
Zuboff, S. (2019). “We make them dance”: Surveillance capitalism, the rise of instrumentarian power, and the threat to human rights. In R. F. Jørgensen (Ed.), Human rights in the age of platforms (pp. 3–20). MIT Press.
Kazansky, B. (2021). “It depends on your threat model”: The anticipatory dimensions of resistance to data-driven surveillance. Big Data & Society, 8(1), 1–12.

Unit 4—Digital Materialities: Getting to Know the Machine

Blanchette, J.- F. (2011). A material history of bits. Journal of the American Society for Information Science and Technology, 62(6), 1042–1057.
Pasek, A. (2023). From atoms to electrons: An energy history and future of computing. In A. Pasek, C. K. Lin, Z. Griffin, T. Cooper, & J. B. Kinder (Eds.), Digital energetics (pp. 15–42). Meson Press; University of Minnesota Press.

Unit 5—Code Studies Part I: Analyzing the Discourse of Digital Instructions

Manovich, L. (2013). Introduction. In Software takes command. Bloomsbury Academic.

Please read pages 1–24 of the Introduction.
Kitchin, R., & Dodge, M. (2011). The nature of software. In Code/space: Software and everyday life (pp. 23–44). MIT Press.

Unit 6—Code Studies Part II: Exclusions & Repressions

Easter, B. (2020). Fully human, fully machine: Rhetorics of digital disembodiment in programming. Rhetoric Review, 39(2), 202–215.
Klinger, U., & Svensson, J. (2021). The power of code: Women and the making of the digital world. Information, Communication & Society, 24(14), 2075–2090.
Paré, D. (in press/2021). Queering computing and computing education. In Oxford Research Encyclopedia of Education.

Unit 7—Data and Algorithms: The Problems of Bias and Distortion

Gottlieb, B. (2018). DIG (Digitalization). In Digital materialism: Origins, philosophies, prospects (pp. 53–64). Emerald Publishing.
Van Brakel, R. E. (2021). Rethinking predictive policing: Towards a holistic framework of democratic algorithmic surveillance. In M. Schuilenburg & R. Peeters (Eds.), Algorithmic societies: Power, knowledge, and technology in the age of algorithms (pp. 104–118). Routledge.
Walter, M., Lovett, R., Maher, B., Williamson, B., Prehn, J., Bodkin‐Andrews, G., & Lee, V. (2021). Indigenous data sovereignty in the era of big data and open data. Australian Journal of Social Issues, 56(2), 143–156.
Munn, L. (2018). Unravelling the algorithmic. In Ferocious logics: Unmaking the algorithm (pp. 9–26). Meson Press.

Unit 8—Artificial Intelligence Part I: Introduction

Kaplan, A. (2021). Artificial intelligence (AI): When humans and machines might have to coexist. In P. Verdegem (Ed.), AI for Everyone?: Critical Perspectives (pp. 21–32). University of Westminster Press.
Lewis, J. E. (Ed.). (2020). Indigenous protocol and artificial intelligence [Position paper]. Honolulu, Hawai’i: The Initiative for Indigenous Futures and the Canadian Institute for Advanced Research (CIFAR).

Unit 9—Artificial Intelligence Part II: Limitations & Parameters

Floridi, L. (2019). What the near future of artificial intelligence could be. Philosophy & Technology, 32(1), 1–15.
Adam, A. (2000). Deleting the subject: A feminist reading of epistemology in artificial intelligence. Minds and Machines, 10, 231–253.

Unit 10—Artificial Intelligence Part III: Environmental Implications & Regulation

Shaji George, A., Hovan George, A. S., & Gabrio Martin, A. S. (2023). The environmental impact of AI: A case study of water consumption by ChatGPT. Partners Universal International Innovation Journal, 1(2), 91–104.
Zewe, A. (2025, January 17). Explained: Generative AI’s environmental impact. MIT News.
Stark, L., Greene, D., & Hoffmann, A. L. (2021). Critical perspectives on governance mechanisms for AI/ML systems. In J. Roberge & M. Castelle (Eds.), The cultural life of machine learning: An incursion into critical AI studies (pp. 257–280). Springer International.

Unit 11—Unsettling the Digital Part I: Feminist Datasets & Critical Technical Practices

Bogers, L., & Chiappini L. (Eds.). (2019). The critical makers reader: (Un)learning technology. Institute of Network Cultures.

Please read:

  • “Making Critical Ethical Software” by Caroline Sinders (pp. 87–94)
  • “Teaching Critical Technical Practice” by Graham Harwood (pp. 29–37)
Costanza-Chock, S. (2018). Design justice, A.I., and escape from the matrix of domination. Journal of Design and Science (JoDS).

Unit 12—Unsettling the Digital Part II: Critical Engagements

Zeilinger, M. (2021). What does AI hack? Scaffolding for a critical art of AI. In Tactical entanglements: AI art, creative agency, and the limits of intellectual property (pp. 33–56). Meson Press.
Reddy, A. (2022). Artificial everyday creativity: Creative leaps with AI through critical making. Digital Creativity, 33(4), 295–313.

Assignment 3: Essay—Critical Data Literacies

Brand, J., & Sander, I. (2020). Critical data literacy tools for advancing data justice: A guidebook. Data Justice Lab.