Thilo Hagendorff

Eine komplette Liste aller meiner Publikationen und Bücher findet sich hier. Die folgende Liste ist eine Auswahl:

• Vaugrante, Laurène; Niepert, Mathias; Hagendorff, Thilo (2024): A Looming Replication Crisis in Evaluating Behavior in Language Models? Evidence and Solutions. In arXiv:2409.20303, pp. 1–23. (Link)
• Hagendorff, Thilo (2024): Mapping the Ethics of Generative AI. A Comprehensive Scoping Review. In Minds and Machines 34 (39), 1–27. (Link)
• Hagendorff, Thilo; Dasgupta, Ishita; Binz, Marcel; Chan, Stephanie C. Y.; Lampinen, Andrew; Wang, Jane X. et al. (2024): Machine Psychology. In arXiv:2303.13988, pp. 1–17. (Link)
• Hagendorff, Thilo (2024): Deception abilities emerged in large language models. In Proceedings of the National Academy of Sciences 121 (24), 1-8. (Link)
• Meding, Kristof; Hagendorff, Thilo (2024): Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithms. In Philosophy & Technology 37 (1), pp. 1–22. (Link)
• Hagendorff, Thilo; Fabi, Sarah; Kosinski, Michal (2023): Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in ChatGPT. In Nature Computational Science 3 (10), pp. 833–838. (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 ist Experte für KI-Ethik, Maschinenverhalten in Sprachmodellen sowie für die Schnittstelle zwischen Psychologie und maschinellem Lernen. Er arbeitet als unabhängiger Forschungsgruppenleiter an der Universität Stuttgart. Zuvor arbeitete er für den Exzellenzcluster "Maschinelles Lernen" an der Universität Tübingen. Er war Gastwissenschaftler an der Stanford University sowie an der UC San Diego. Als Dozent lehrt er u.a. am Hasso-Plattner-Institut in Potsdam.

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