Bibliographie

TippHinweis: Urheberrecht

Wir bemühen uns, frei zugängliche Literatur zu verwenden. Sollte dies nicht möglich sein, bitten wir Sie, die Literatur über die Bibliotheksplattform LIBER Libraries zu beziehen. Von der Verwendung von Plattformen wie Anna’s Archive oder Library Genesis raten wir trotz stichhaltiger Argumente ab.

Siehe Karaganis (2018)

Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, und 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, und 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, Dezember. 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, und Lauren F. Klein. 2023. Data Feminism. First MIT Press paperback edition. Cambridge, Massachusetts: The MIT Press. https://data-feminism.mitpress.mit.edu/.

Drage, Eleanor, und 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, und 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, Hrsg. 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, und Yacine Jernite. 2023. „Stable Bias: Analyzing Societal Representations in Diffusion Models“. 9. November 2023. https://doi.org/10.48550/arXiv.2303.11408.

Mattu, Surya, Julia Angwin, Jeff Larson, und 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, und 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, Februar, 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, und Cindarella Petz. 2025. „Working Paper: Implementing Generative AI in the Historical Studies“, Februar. https://doi.org/10.5281/zenodo.14924737.

Offert, Fabian, und Ranjodh Singh Dhaliwal. 2024. „The Method of Critical AI Studies, A Propaedeutic“. 10. Dezember 2024. https://doi.org/10.48550/arXiv.2411.18833.

Ouyang, Long u. a. 2022. „Training Language Models to Follow Instructions with Human Feedback“. 4. März 2022. https://doi.org/10.48550/arXiv.2203.02155.

Penedo, Guilherme u. a. 2024. „The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale“. 31. Oktober 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., und Peter Norvig. 2020. Artificial Intelligence: A Modern Approach. 4. Aufl. 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/.

Hinweis

Diese Seite listet alle Referenzen auf, die im Projekt Critical AI Literacy für Historiker:innen zitiert werden. Die Bibliographie wird automatisch aus allen Zitaten generiert, die in den Übungen und der Dokumentation verwendet werden.