The website will soon be reconstructed. I recently moved to the Technical University of Nuremberg, as a part of Claire Vernade's group.
For the time being, you can reach me at thomas.grote(at)utn.de
The website will soon be reconstructed. I recently moved to the Technical University of Nuremberg, as a part of Claire Vernade's group.
For the time being, you can reach me at thomas.grote(at)utn.de
Recent Publications:
Grote, T. (forthcoming): Medical Artificial Intelligence. In The Oxford Handbook of Philosophy of Medicine (ed. by A. Broadbent). OUP
Grote, T. & Buchholz, O. (forthcoming): Machine Learning in Public Health and the Prediction-Intervention Gap. In Philosophy of Science for Machine Learning: Core Issues and New Perspectives (ed. by J. Durán and G. Pozzi), Springer (Synthese Library).
Grote, T. (forthcoming): How is AI ethics distinct (when compared to bioethics)? In Contemporary Debates in the Ethics of AI (ed. by Sven Nyholm, John Zerilli and Atoosa Kasirzadeh). Wiley.
Grote, T. & Paulo, N. (forthcoming): A Minimalist Account of the Right to Explanation. Philosophy & Technology.
Grote, T., Freiesleben, T. & Berens, P. (2024): Foundation Models in Healthcare Require Rethinking Reliability. Nature Machine Intelligence. Link
Genin, K., Grote, T. & Wolfers, T. (2024): Computational Psychiatry and the Evolving Concept of a Mental Disorder. Synthese.
Grote, T., Genin. K. & Sullivan, E. (2024): Reliability in Machine Learning. Philosophy Compass.
Grote. T. & Berens, P. (2024): A paradigm shift? -- On the ethics of medical large language models. Bioethics.
Grote, T. (2024): Machine Learning in Healthcare and the Methodological Priority of Epistemology over Ethics. Inquiry
Buchholz, O. & Grote, T. (2023): Predicting and Explaining with Machine Learning Models: Social Science as a Touchstone. Studies in History and Philosophy of Science.
Freiesleben, T. & Grote, T. (2023): Beyond Generalization: A Theory of Robustness in Machine Learning. Synthese.
Grote, T. (2023): The Allure of Simplicity: On Interpretable Machine Learning Models in Healthcare. Philosophy of Medicine.
Grote, T. (2023). Fairness as adequacy: a sociotechnical view on model evaluation in machine learning. AI and Ethics, 1-14.
Grote, T & Berens, P. (2023): Uncertainty, Evidence, and the Integration of Machine Learning into Medical Practice. The Journal of Medicine and Philosophy.
Grote, T., & Broadbent, A. (2022). Machine learning and public health: Philosophical issues. In The Routledge Handbook of Philosophy of Public Health (pp. 190-204). Routledge.
Grote T & Keeling. G. (2022): Enabling fairness in healthcare through machine learning. Ethics and Information Technology.
Broadbent. A. & Grote, T. (2022): Can Robots Do Epidemiology? Machine learning, causal inference, and predicting the outcomes of public health interventions. Philosophy & Technology.
Grote, T & Keeling, G. (2022): On Algorithmic Fairness in Medical Practice. Cambridge Quarterly of Healthcare Ethics.
Grote, T. & Berens. P. (2021): How Competitors Become Collaborators -- Bridging the Gap(s) Between Machine Learning Algorithms and Clinicians. Bioethics.
Genin, K. & Grote, T. (2021): Randomized Controlled Trials in Medical AI -- A Methodological Critique. Philosophy of Medicine.
Grote, T. (2021): Randomised Controlled Trials in Medical AI: Ethical Considerations. Journal of Medical Ethics.
Grote, T. (2021): Trustworthy Medical AI Systems Need to Know When They Don`t Know. Journal of Medical Ethics.
Grote, T., & Di Nucci, E. (2020). Algorithmic decision-making and the problem of control. Technology, anthropology, and dimensions of responsibility. Metzler (edited by Michael Kühler and Birgit Beck), 97-113.
Grote, T. & Berens, P. (2020): On the Ethics of Algorithmic Decision-Making in Healthcare. Journal of Medical Ethics 46(3), 205-11
Contact: thomas.grote@uni-tuebingen.de