Source: Critical AI Literacy in Practice · Zenodo Authors: Mähr, Moritz Licence: CC-BY-4.0 — https://creativecommons.org/licenses/by/4.0/
Critical AI Literacy in Practice
Lessons from Current DH Projects
Moritz Mähr moritz.maehr@gmail.com University of Basel University of Bern
September 9, 2025
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AI is Everywhere, also in Science
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AI in Publications
Article 3 / 32
AI in Manuscripts
Article 4 / 32
AI in Experiments
Article 5 / 32
AI in Experiments
Article 6 / 32
What can we do about it?
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We can
- Understand the technology and its history 8 / 32
A short history of AI
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Theoretical AI
1950: Imitation Game
Paper 10 / 32
Theoretical AI
1950: Imitation Game
The new form of the problem can be described in terms of a game which we call the ‘imitation game’. It is played with three people, a man
(A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart from the other two. (…) We now ask the question, ’What will happen when a machine takes the part of A in this game? 11 / 32
Symbolic AI
1966: ELIZA
BBC footage 12 / 32
Rule-based AI
Until late 1980s: Expert Systems & Machine Translation
The Selectric on display in the IBM pavilion at the 1964-65 World’s Fair in New York. 13 / 32
Statistical AI
Late 1990s/early 2000s: Topic Modeling & Data Mining
Topic Modeling 14 / 32
Neural AI
2010s: Deep Learning & Large Language Models
Word2Vec 15 / 32
Generative AI
Today: Generative AI & Foundation Models
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Known problems of Generative AI
Bias in training data Lack of explainability Lack of transparency Lack of accountability Lack of reproducibility Environmental impact Ethical issues Legal issues Social issues Epistemological issues … 17 / 32
What can we do about it?
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We can
- Understand the technology and its history
- Understand the limitations and problems of AI 19 / 32
Critical AI Studies
Paper 20 / 32
Teaching AI Literacy
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Critical AI Literacy
Technical literacy: Understanding how AI systems work, their capabilities and limitations Epistemological awareness: Questioning what counts as knowledge and how AI shapes it Ethical evaluation: Considering consent, privacy, transparency, and accountability Social impact assessment: Examining power structures, equity, and broader implications Practical application: Developing workflows that maintain scholarly rigor Continuous learning: Staying informed as technology evolves rapidly
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Decoding Inequality (UniBe)
Course Description 23 / 32
ChatGPT and Beyond (UZH)
Course Description 24 / 32
What can we do about it?
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We can
- Understand the technology and its history
- Understand the limitations and problems of AI
- Make better use of AI tools 26 / 32
DH in Action: Swiss Projects Using LLMs (Tools &
Platforms)
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Re-Experiencing History with AI (UZH)
Project 28 / 32
Data visualization to access cultural archives (SUPSI & ETH Library)
Project 29 / 32
Generating alt text for historical sources and objects (Stadt.Geschichte.Basel, University of Basel)
Project 30 / 32
LLM benchmarking for humanities tasks (RISE, UNIBAS)
Project 31 / 32
Bibliography
Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. «On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜». In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–23. FAccT ’21. New York, NY, USA: Association for Computing Machinery, 2021. https://doi.org/10.1145/3442188.3445922. Long, Duri, and Brian Magerko. «What Is AI Literacy? Competencies and Design Considerations». In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. CHI ’20. New York, NY, USA: Association for Computing Machinery, 2020. https://doi.org/10.1145/3313831.3376727. Loukissas, Yanni A. All Data Are Local: Thinking Critically in a Data-Driven Society. Cambridge, Massachusetts: The MIT Press, 2019. https://doi.org/10.7551/mitpress/11543.001.0001. Mueller, Milton L. «It’s Just Distributed Computing: Rethinking AI Governance». Telecommunications Policy, Februar 2025, 102917. https://doi.org/10.1016/j.telpol.2025.102917. O’Neil, Cathy. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. First edition. New York: Crown Publishing Group, 2016. Offert, Fabian, and Ranjodh Singh Dhaliwal. «The Method of Critical AI Studies, A Propaedeutic», 10. Dezember 2024. https://doi.org/10.48550/arXiv.2411.18833.
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