Bibliographie
AstuceRemarque: droits d’auteur
Nous nous efforçons d’utiliser des ouvrages librement accessibles. Si cela n’est pas possible, nous vous prions de vous procurer les ouvrages via la plateforme bibliothécaire LIBER Libraries. Malgré des arguments valables, nous déconseillons l’utilisation de plateformes telles que Anna’s Archive ou Library Genesis.
Voir Karaganis (2018)
Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, et Shmargaret Shmitchell. 2021. « On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜 ». In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’21), 610‑23. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3442188.3445922.
Buolamwini, Joy. 2023. Unmasking AI: A Story of Hope and Justice in a World of Machines. First edition. New York: Random House.
Buolamwini, Joy, et Timnit Gebru. 2018. « Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification ». In Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 81:77‑91. PMLR. https://proceedings.mlr.press/v81/buolamwini18a.html.
Campbell, Chris. 2025. « The Historian in the Age of AI ». Transactions of the Royal Historical Society, décembre. https://doi.org/10.1017/S0080440125100509.
Chen, Zhisheng. 2023. « Ethics and Discrimination in Artificial Intelligence-Enabled Recruitment Practices ». Humanities and Social Sciences Communications 10 (1): 1‑12. https://doi.org/10.1057/s41599-023-02079-x.
D’Ignazio, Catherine. 2024. Counting Feminicide: Data Feminism in Action. The MIT Press. https://doi.org/10.7551/mitpress/14671.001.0001.
D’Ignazio, Catherine, et Lauren F. Klein. 2023. Data Feminism. First MIT Press paperback edition. Cambridge, Massachusetts: The MIT Press. https://data-feminism.mitpress.mit.edu/.
Drage, Eleanor, et Kerry Mackereth. 2022. « Does AI Debias Recruitment? Race, Gender, and AI’s “Eradication of Difference” ». Philosophy & Technology 35 (4): 89. https://doi.org/10.1007/s13347-022-00543-1.
Fickers, Andreas, et Juliane Tatarinov. 2022. « Digital Source Criticism: The Case of the Historian’s Craft in the Digital Age ». Journal of Digital History 2 (1): 1‑20. https://doi.org/10.1515/jdh-2022-0001.
Karaganis, Joe, éd. 2018. Shadow Libraries: Access to Knowledge in Global Higher Education. Cambridge, MA: The MIT Press. https://doi.org/10.7551/mitpress/11339.001.0001.
Karpathy, Andrej. 2025. Deep Dive into LLMs like ChatGPT. https://www.youtube.com/watch?v=7xTGNNLPyMI.
Luccioni, Alexandra Sasha, Christopher Akiki, Margaret Mitchell, et Yacine Jernite. 2023. « Stable Bias: Analyzing Societal Representations in Diffusion Models ». 9 novembre 2023. https://doi.org/10.48550/arXiv.2303.11408.
Mattu, Surya, Julia Angwin, Jeff Larson, et Lauren Kirchner. 2016. « Machine Bias ». ProPublica. 2016. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.
Mehrabi, Ninareh, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, et Aram Galstyan. 2021. « A Survey on Bias and Fairness in Machine Learning ». ACM Computing Surveys 54 (6): 115:1‑35. https://doi.org/10.1145/3457607.
Milligan, Ian. 2019. History in the Age of Abundance? How the Web Is Transforming Historical Research. Montreal; Kingston: McGill-Queen’s University Press. https://doi.org/10.2307/j.ctvggx2kh.
Mueller, Milton L. 2025. « It’s Just Distributed Computing: Rethinking AI Governance ». Telecommunications Policy, février, 102917. https://doi.org/10.1016/j.telpol.2025.102917.
Noble, Safiya Umoja. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. New York: NYU Press.
O’Neil, Cathy. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown Publishing Group.
Oberbichler, Sarah, et Cindarella Petz. 2025. « Working Paper: Implementing Generative AI in the Historical Studies », février. https://doi.org/10.5281/zenodo.14924737.
Offert, Fabian, et Ranjodh Singh Dhaliwal. 2024. « The Method of Critical AI Studies, A Propaedeutic ». 10 décembre 2024. https://doi.org/10.48550/arXiv.2411.18833.
Ouyang, Long et al. 2022. « Training Language Models to Follow Instructions with Human Feedback ». 4 mars 2022. https://doi.org/10.48550/arXiv.2203.02155.
Penedo, Guilherme et al. 2024. « The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale ». 31 octobre 2024. https://doi.org/10.48550/arXiv.2406.17557.
Putnam, Lara. 2016. « The Transnational and the Text-Searchable: Digitized Sources and the Shadows They Cast ». The American Historical Review 121 (2): 377‑402. https://doi.org/10.1093/ahr/121.2.377.
Russell, Stuart J., et Peter Norvig. 2020. Artificial Intelligence: A Modern Approach. 4ᵉ éd. Hoboken, NJ: Pearson.
The Turing Way Community. 2022. « The Turing Way: A Handbook for Reproducible, Ethical and Collaborative Research ». Zenodo. 2022. https://doi.org/10.5281/zenodo.3233853.
Zuboff, Shoshana. 2017. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. New York, NY: PublicAffairs. https://www.hachettebookgroup.com/titles/shoshana-zuboff/the-age-of-surveillance-capitalism/9781610395694/.
Note
Cette page répertorie toutes les références citées dans le projet Critical AI Literacy pour les Historiens. La bibliographie est automatiquement générée à partir de toutes les citations utilisées dans les exercices et la documentation.