BMET 27A01 - AI Data and Democracy in North America and Europe

Across Canada, the United States, and Europe, the same words reappear: trust, transparency, ethics. We will ask what they actually do – and whom they exclude. This is not a policy course. It is a course about the stories that live inside policies, about how documents dream of neutrality, and how code repeats that dream. We will examine AI systems not as tools but as fictions of order. Regulation drafts will be read like screenplays, datasets like novels, and algorithms like political theatre. Each week moves between institutions and artists: Laurie Anderson's memory machines, Adam Curtis's documentary montages, and my own experience working with AI archives and bureaucratic infrastructures. Students are invited to write, design, or code their way through these questions. Expect a seminar that is half theory, half creative lab – sometimes messy, often alive. Academic expectations Come prepared, curious, and willing to experiment. Participation matters more than performance. Each week, students bring a short “trace”: a quote, an image, a dataset, or a brief observation. The course includes one concise oral presentation (10 minutes) and a final mini-audit – a creative and critical analysis of an AI system, interface, or document. No technical background is required.
Rony FERAT
Atelier
English
Spring 2025-2026
Active participation and weekly traces – 30 % In-class presentation – 20 % Final mini-audit (group or solo) – 50 % Grades reward precision and originality rather than technical jargon.
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Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT'21). A
Crawford, Kate. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press, 2021.
Gebru, Timnit, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. Datasheets for Datasets. Communications of the ACM 64, no. 12 (2021): 86– 92.
Noble, Safiya Umoja. Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press, 2018.