I am a research fellow at the "Ethics and Philosophy Lab" of the Cluster of Excellence "Machine Learning: New Perspectives for Science" at the University of Tübingen. My background is in philosophy and my research focusses on issues in machine learning, lying at the intersection of ethics and philosophy of science. In particular, I am interested in problems of interpretability, fairness, and model evaluation -- with an emphasis on the medical domain. More recently, I am also working on the use of predictive models in the social sciences. If you want to know more about my research or have ideas for collaborations, feel free to get in touch.
Together with Eric Raidl, I am also co-supervisor in the project "Artificial Intelligence, Trustworthiness, and Explainability" (AITE) funded by the Baden-Württemberg Stiftung.
I am a co-supervisor in the "Certification and Foundations of Safe Machine Learning Systems in Healthcare” project, funded by the Carl-Zeiss-Stiftung. A PhD position, to work on issues of fairness in healthcare is currently advertised.
I am also a co-supervisor in the Cluster`s MiniGrad School "Machine Learning in Education".
News: The next iteration of "Philosophy of Science Meets Machine Learning" will take place from October 20-22 in Tübingen. The workshop will be organized by Konstantin Genin and myself. More news to follow soon.
Grote. T & Keeling. G. (forthcoming): 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. & Berens. P. (2021): How Competitors Become Collaborators -- Bridging the Gap(s) Between Machine Learning Algorithms and Clinicians. Bioethics.
Genin, K. & Grote, T. (2021): Randomised 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 & Keeling, G. (2022): "On Algorithmic Fairness in Medical Practice". Cambridge Quarterly of Healthcare Ethics.
Grote, T & Berens, P. (forthcoming): "Uncertainty, Evidence, and the Integration of Machine Learning into Medical Practice". The Journal of Medicine and Philosophy.
Grote, T. & Berens, P. (2020): "On the Ethics of Algorithmic Decision-Making in Healthcare". Journal of Medical Ethics 46(3), 205-11
Talks in 2022:
February: Talk on Fairness and Model Evaluation (Jülich)
March: Talk on Model Interpretability in Healthcare (CAIS -- Bochum)
Mai: Talk on Model Interpretability in Healthcare (Mannheim)
Mai: Talk on Model Interpretability in Healthcare (BBMI-ERIC -- Berlin)
June: Talk on Algorithmic Decision-Making at the MPI for Human Cognitive and Brain Sciences (Leipzig)
July: Talk on Model Interpretability in Healthcare (SPSP -- Ghent)
July: Talk on ML and Public Health (Digital Ethics Workshop -- Hannover)