Project-Related Publications
- Boge, F. J. (2024). Re-Assessing the Experiment / Observation-Divide, Philosophy of Science, do: 10.1017/psa.2024.23
- Mosig, A. and Boge, F. J. (2024). Causality and Scientific Explanation of Artificial Intelligence Systems in Biomedicine, European Journal of Physiology, doi:10.1007/s00424-024-03033-9
- Boge, F. J. and de Regt, H. W. (forthcoming). Machine Learning Discoveries and Scientific Understanding in Particle Physics: Problems and Prospects, in: Duran, J. M., and Pozzi, G. (Eds.), Philosophy of Science for Machine Learning, Synthese Library, Springer
- Boge, F. J. (forthcoming). Functional Concept Proxies and the Actually Smart Hans Problem: What’s Special About Deep Neural Networks in Science, Synthese, doi: 10.1007/s11229-023-04440-8
- Boge, F. J. (2022). Two Dimensions of Opacity and the Deep Learning Predicament, Minds and Machines, 32(1), pp. 43-75, doi: 10.1007/s11023-021-09569-4
- Boge, F. J., Hillerbrand R., and Grünke, P. (2022). Introduction: Machine Learning: Prediction Without Explanation? Minds and Machines, 32(1), pp. 1-11, doi: 10.1007/s11023-022-09597-8
- Boge, F. J., Hillerbrand R., and Grünke, P. (2022). Machine Learning: Prediction Without Explanation?, Minds and Machines Special Issue, 32(1)
- Boge, F. J. (2021). Why Trust a Simulation? Models, Parameters, and Robustness in Simulation-Infected Experiments, British Journal for the Philosophy of Science, doi: 10.1086/716542
- Boge, F. J. and Poznic, M. (2021). Meeting report: Machine Learning and the Future of Scientific Explanation, Journal for General Philosophy of Science 52(1), 171–176, doi: 10.1007/s10838-020-09537-z
- Boge, F. J. and Grünke, P. (2019). Computer Simulations, Machine Learning and the Laplacean Demon: Opacity in the Case of High Energy Physics, forthcoming in Resch, Kaminski, and Gehring (Eds.), The Science and Art of Simulation II, Springer
