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Thilo Hagendorff


Research Group - Ethics of Generative AI


Universitätsstraße 32
70569 Stuttgart
Room: 00.123


  • AI Ethics
  • Machine Behavior / Machine Psychology
  • Large Language Models

A complete list of my publications as well as books can be found here. The following is a selection:

  • Hagendorff, Thilo; Fabi, Sarah; Kosinski, Michal (2023): Thinking Fast and Slow in Large Language Models. In arXiv:2212.05206, pp. 1–30. (Link)
  • Hagendorff, Thilo (2023): Deception Abilities Emerged in Large Language Models. In arXiv:2307.16513, pp. 1–21. (Link)
  • Hagendorff, Thilo (2023): Machine Psychology: Investigating Emergent Capabilities and Behavior in Large Language Models Using Psychological Methods. In arXiv:2303.13988v1, pp. 1–15. (Link)
  • Hagendorff, Thilo; Fabi, Sarah (2023): Why we need biased AI: How including cognitive biases can enhance AI systems. In Journal of Experimental & Theoretical Artificial Intelligence, pp. 1–14. (Link)
  • Hagendorff, Thilo; Bossert, Leonie N.; Tse, Yip Fai; Singer, Peter (2023): Speciesist bias in AI: how AI applications perpetuate discrimination and unfair outcomes against animals. In AI Ethics 3 (3), pp. 717–734. (Link)
  • Hagendorff, Thilo (2022): A Virtue-Based Framework to Support Putting AI Ethics into Practice. In Philosophy & Technology 35 (3), pp. 1–246. (Link)
  • Hagendorff, Thilo; Danks, David (2022): Ethical and methodological challenges in building morally informed AI systems. In AI Ethics, pp. 1–14. (Link)
  • Hagendorff, Thilo (2022): AI ethics and its pitfalls: not living up to its own standards? In AI and Ethics, pp. 1–8. (Link)
  • Hagendorff, Thilo (2022): Blind spots in AI ethics. In AI Ethics 2 (4), pp. 851–867. (Link)
  • Hagendorff, Thilo (2021): Linking human and machine behavior. A new approach to evaluate training data quality for beneficial machine learning. In Minds and Machines 31, pp. 563–593. (Link)
  • Hagendorff, Thilo; Meding, Kristof (2021): Ethical considerations and statistical analysis of industry involvement in machine learning research. In AI & SOCIETY - Journal of Knowledge, Culture and Communication, pp. 1–11. (Link)
  • Hagendorff, Thilo (2021): Forbidden knowledge in machine learning. Reflections on the limits of research and publication. In AI & SOCIETY - Journal of Knowledge, Culture and Communication 36 (3), pp. 767–781. (Link)
  • Helm, Paula; Hagendorff, Thilo (2021): Beyond the Prediction Paradigm. Challenges for AI in the Struggle Against Organized Crime. In Law and Contemporary Problems 84 (3), pp. 1–17. (Link)
  • Hagendorff, Thilo (2020): The Ethics of AI Ethics. An Evaluation of Guidelines. In: Minds and Machines 30 (3), pp. 457–461. (Link)
  • Hagendorff, Thilo (2019): From privacy to anti-discrimination in times of machine learning. In: Ethics and Information Technology 33 (3), pp. 331–343. (Link)
  • Hagendorff, Thilo; Wezel, Katharina (2019): 15 challenges for AI: or what AI (currently) can’t do. In AI & SOCIETY - Journal of Knowledge, Culture and Communication 35 (2), pp. 355-365. (Link)

Dr. Thilo Hagendorff is an expert in AI ethics, machine behavior in language models, as well as the intersection of machine learning and psychology. He is working as an Independent Research Group Leader at the University of Stuttgart. Previously, he worked for the Cluster of Excellence “Machine Learning” at the University of Tuebingen. He was a visiting scholar at Stanford University as well as UC San Diego. As a lecturer, he teaches at the Hasso Plattner Institute in Potsdam, among others.

More details can be found here.

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