Bibliography

TipNote: Copyright

We strive to use freely accessible literature. If this is not possible, we ask you to obtain the literature via the library platform LIBER Libraries. Despite valid arguments, we advise against using platforms such as Anna’s Archive or Library Genesis.

See Karaganis (2018)

Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. “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) (New York, NY, USA), March 1, 610–23. https://doi.org/10.1145/3442188.3445922.

Bundesgesetz Über Den Datenschutz (DSG) (2020). https://www.fedlex.admin.ch/eli/cc/2022/491/de.

Buolamwini, Joy. 2023. Unmasking AI: A Story of Hope and Justice in a World of Machines. First edition. Random House.

Buolamwini, Joy, and Timnit Gebru. 2018. “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification.” Proceedings of the 1st Conference on Fairness, Accountability and Transparency, PMLR, vol. 81 (January): 77–91. https://proceedings.mlr.press/v81/buolamwini18a.html.

Campbell, Chris. 2025. “The Historian in the Age of AI.” Transactions of the Royal Historical Society, ahead of print, December 10. https://doi.org/10.1017/S0080440125100509.

Carroll, Stephanie Russo, Ibrahim Garba, Oscar L. Figueroa-Rodríguez, et al. 2020. “The CARE Principles for Indigenous Data Governance.” Data Science Journal 19 (1): 43. https://doi.org/10.5334/dsj-2020-043.

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, and Lauren F. Klein. 2023. Data Feminism. First MIT Press paperback edition. The MIT Press. https://data-feminism.mitpress.mit.edu/.

Deutsche Forschungsgemeinschaft. 2023. Stellungnahme Des Präsidiums Der Deutschen Forschungsgemeinschaft (DFG) Zum Einfluss Generativer Modelle Für Die Text- Und Bilderstellung Auf Die Wissenschaften Und Das Förderhandeln Der DFG. Deutsche Forschungsgemeinschaft. https://www.dfg.de/resource/blob/289674/ff57cf46c5ca109cb18533b21fba49bd/230921-stellungnahme-praesidium-ki-ai-data.pdf.

Díez García, Lucía, Paula Terleira Fernández, Valentín Cardeñoso-Payo, André F. Sales Mendes, and Álvaro Lozano Murciego. 2025. “Evaluating Vision Language Models for Handwritten Text Recognition.” In New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence. Advances in Intelligent Systems and Computing. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-99474-6_24.

Drage, Eleanor, and 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.

Drucker, Johanna. 2011. “Humanities Approaches to Graphical Display.” Digital Humanities Quarterly 5 (1). https://doi.org/10.63744/r4ysrh7ae534.

European Commission, Directorate-General for Research and Innovation. 2026. Living Guidelines on the Responsible Use of Generative AI in Research: ERA Forum Stakeholders’ Document. Third version. European Commission. https://research-and-innovation.ec.europa.eu/document/download/2b6cf7e5-36ac-41cb-aab5-0d32050143dc_en?filename=ec_rtd_ai-guidelines.pdf.

European Data Protection Board. 2026. Guidelines 1/2026 on Processing of Personal Data for Scientific Research Purposes. Version 1.0. European Data Protection Board. https://www.edpb.europa.eu/our-work-tools/documents/public-consultations/2026/guidelines-12026-processing-personal-data_en.

Fickers, Andreas. 2020. “Update Für Die Hermeneutik. Geschichtswissenschaft Auf Dem Weg Zur Digitalen Forensik?” Zeithistorische Forschungen / Studies in Contemporary History 17 (1): 157–68. https://doi.org/10.14765/zzf.dok-1765.

Fickers, Andreas, and 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.

Historisches Institut, Universität Bern. 2026. “Richtlinien Zur Verwendung von KI-Gestützten Programmen.” Historisches Institut, Universität Bern, August 14. https://www.hist.unibe.ch/studium/plagiate__ki_gebrauch/index_ger.html.

Hodel, Tobias, David Schoch, Christa Schneider, and Jake Purcell. 2021. “General Models for Handwritten Text Recognition: Feasibility and State-of-the Art. German Kurrent as an Example.” Journal of Open Humanities Data 7 (July): 13. https://doi.org/10.5334/johd.46.

International Committee of Medical Journal Editors. 2026. “Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals.” Versions Updated January 2026. January 1. https://www.icmje.org/recommendations/.

Kapoor, Sayash, Benedikt Stroebl, Zachary S. Siegel, Nitya Nadgir, and Arvind Narayanan. 2024. “AI Agents That Matter.” July 1. https://doi.org/10.48550/arXiv.2407.01502.

Karaganis, Joe, ed. 2018. Shadow Libraries: Access to Knowledge in Global Higher Education. 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.

Lang, Sarah, and Elena Suárez Cronauer. 2026. “Beyond Data Feminism. Towards Ethical Data Work in the (Digital) Humanities.” Zeitschrift Für Digitale Geisteswissenschaften – Working Papers, no. 4 (February). https://doi.org/10.17175/wp_2026.

Lewis, Patrick, Ethan Perez, Aleksandra Piktus, et al. 2020. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” May 22. https://doi.org/10.48550/arXiv.2005.11401.

Luccioni, Alexandra Sasha, Christopher Akiki, Margaret Mitchell, and Yacine Jernite. 2023. “Stable Bias: Analyzing Societal Representations in Diffusion Models.” November 9. https://doi.org/10.48550/arXiv.2303.11408.

Luccioni, Sasha, Yacine Jernite, and Emma Strubell. 2024. “Power Hungry Processing: Watts Driving the Cost of AI Deployment?” Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’24) (New York, NY, USA), June 3, 85–99. https://doi.org/10.1145/3630106.3658542.

Mattu, Surya, Julia Angwin, Jeff Larson, and Lauren Kirchner. 2016. “Machine Bias.” ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.

Meadows, R. Darrell, and Joshua Sternfeld. 2023. “Artificial Intelligence and the Practice of History.” The American Historical Review 128 (3): 1345–49. https://doi.org/10.1093/ahr/rhad362.

Mehrabi, Ninareh, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and 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. 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, February, 102917. https://doi.org/10.1016/j.telpol.2025.102917.

Noble, Safiya Umoja. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.

O’Neil, Cathy. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing Group.

Oberbichler, Sarah, and Cindarella Petz. 2025. Working Paper: Implementing Generative AI in the Historical Studies. Version 1.0. February 25. https://doi.org/10.5281/zenodo.14924737.

Offert, Fabian, and Ranjodh Singh Dhaliwal. 2024. “The Method of Critical AI Studies, a Propaedeutic.” December 10. https://doi.org/10.48550/arXiv.2411.18833.

Ouyang, Long et al. 2022. “Training Language Models to Follow Instructions with Human Feedback.” March 4. 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.” October 31. 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.

Risam, Roopika, and Alex Gil. 2022. “Introduction: The Questions of Minimal Computing.” Digital Humanities Quarterly 16 (2). https://digitalhumanities.org/dhq/vol/16/2/000646/000646.html.

Romein, Christel Annemieke, Achim Rabus, Gundram Leifert, and Phillip Benjamin Ströbel. 2025. “Assessing Advanced Handwritten Text Recognition Engines for Digitizing Historical Documents.” International Journal of Digital Humanities 7 (1): 115–34. https://doi.org/10.1007/s42803-025-00100-0.

Russell, Stuart J., and Peter Norvig. 2020. Artificial Intelligence: A Modern Approach. 4th ed. Pearson.

Spirling, Arthur. 2023. “Why Open-Source Generative AI Models Are an Ethical Way Forward for Science.” Nature 616 (7957): 413. https://doi.org/10.1038/d41586-023-01295-4.

Stark, Luke. 2023. “Artificial Intelligence and the Conjectural Sciences.” BJHS Themes 8 (August): 35–49. https://doi.org/10.1017/bjt.2023.3.

Sternfeld, Joshua. 2023. “AI-as-Historian.” The American Historical Review 128 (3): 1372–77. https://doi.org/10.1093/ahr/rhad368.

Strien, Daniel van, Kaspar Beelen, Mariona Coll Ardanuy, Kasra Hosseini, Barbara McGillivray, and Giovanni Colavizza. 2020. “Assessing the Impact of OCR Quality on Downstream NLP Tasks.” Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) (Setúbal), February 22, 484–96. https://doi.org/10.5220/0009169004840496.

The Turing Way Community. 2022. “The Turing Way: A Handbook for Reproducible, Ethical and Collaborative Research.” Zenodo. https://doi.org/10.5281/zenodo.3233853.

The University of Chicago Press Editorial Staff. 2024. The Chicago Manual of Style. 18th ed. The University of Chicago Press. https://press.uchicago.edu/ucp/books/book/chicago/C/bo213648716.html.

Turpin, Miles, Julian Michael, Ethan Perez, and Samuel R. Bowman. 2023. “Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting.” May 7. https://doi.org/10.48550/arXiv.2305.04388.

Verordnung (EU) 2016/679 Zum Schutz Natürlicher Personen Bei Der Verarbeitung Personenbezogener Daten (Datenschutz-Grundverordnung), Amtsblatt der Europäischen Union 1 (2016). https://eur-lex.europa.eu/eli/reg/2016/679/oj/deu.

Verordnung (EU) 2024/1689 Zur Festlegung Harmonisierter Vorschriften Für Künstliche Intelligenz (Verordnung Über Künstliche Intelligenz), Amtsblatt der Europäischen Union (2024). https://eur-lex.europa.eu/eli/reg/2024/1689/oj/deu.

Walters, William H., and Esther Isabelle Wilder. 2023. “Fabrication and Errors in the Bibliographic Citations Generated by ChatGPT.” Scientific Reports 13 (1): 14045. https://doi.org/10.1038/s41598-023-41032-5.

Wei, Jason, Xuezhi Wang, Dale Schuurmans, et al. 2022. “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.” January 28. https://doi.org/10.48550/arXiv.2201.11903.

Wilkinson, Mark D., Michel Dumontier, IJsbrand Jan Aalbersberg, et al. 2016. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.” Scientific Data 3: 160018. https://doi.org/10.1038/sdata.2016.18.

Zuboff, Shoshana. 2017. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs. https://www.hachettebookgroup.com/titles/shoshana-zuboff/the-age-of-surveillance-capitalism/9781610395694/.

Note

This page lists all references cited throughout the Critical AI Literacy for Historians project. The bibliography is automatically generated from all citations used in the exercises and documentation.