Epistemological Issues of Machine Learning in Science

Date: 27.–28.02.2024

Place: Chaudoire Pavillon, TU Dortmund University

Conference booklet



With impressive advances in Machine Learning (ML) and particularly Deep Learning, Artificial Intelligence is currently taking science by storm. This workshop brings together top scientists and philosophers working on fundamental issues connected to the use of Machine Learning in science. The workshop marks the launch of the DFG-funded Emmy Noether Group UDNN: Scientific Understanding and Deep Neural Networks, and is co-organized with the Lamarr Institute for Machine Learning and Artificial Intelligence and co-funded by the Department for Humanities and Theology at TU Dortmund University.

Topics include, but are not restricted to:

  • The relation between prediction and discovery on the one hand, and explanation and understanding on the other, in fields of science that heavily rely on ML methods
  • The key issues in identifying genuine discoveries and stable predictions by ML systems
  • Core conceptions of “explanation” involved in the field of eXplainable AI (XAI), and their relation to philosophical theories of understanding and explanation
  • Present limitations associated with ML’s predictive power and what may be needed to overcome them
  • The connection between ML and traditional scientific means for prediction and discovery, such as theories, models, and experiments
  • Our present understanding of ML itself and its limitations 


Life Sciences

  • Jürgen Bajorath (University of Bonn)
  • Axel Mosig (Ruhr University Bochum)

Machine Learning Theory

  • M. Klopotek (University of Stuttgart)
  • Marie-Jeanne Lesot (Sorbonne Université)
  • David Watson (King’s College London)


  • Katie Creel (Northeastern University)
  • Brigitte Falkenburg (TU Dortmund)
  • Konstantin Genin (University of Tübingen)
  • Lena Kästner (University of Bayreuth)
  • Henk de Regt (Radbout University Nijmegen)
  • Eva Schmidt (TU Dortmund)
  • Tom Sterkenburg (LMU Munich)

Physics / Astronomy

  • Dominik Elsässer (TU Dortmund)
  • Michael Krämer (RWTH Aachen)
  • Mario Krenn (Max Planck Institute for the Science of Light)
  • Wolfgang Rhode (TU Dortmund)
  • Christian Zeitnitz (BU Wuppertal)

Registration is free but places are limited. To register, please send an E-mail to until January 15, 2024 including your name, institution. A small number of attendees will be able to join the conference dinner on the 27th on a dutch-treat basis. If you want to join the dinner, please indicate this in your registration. 


Day 1 Day 2
09:00–09:15 Arrival + Coffee 09:00–09:15 Arrival + Coffee
09:15-09:20 Opening (FJB) 09:15 – 10:00 From the fair distribution of predictions to the fair distribution of social goods: evaluating the impact of fair machine learning on long-term unemployment
Konstantin Genin, Tübingen
09:20-10:05 Can machines acquire scientific understanding?
Henk de Regt, Nijmegen
10:00 – 10:45 Explainable AI and trustworthy AI: a relation to discuss
Marie-Jeanne Lesot, Paris
10:05–10:50 Richness revisited: clustering and PAC learnability
David Watson, London
10:45 – 11:00 Coffee
10:50–11:05 Coffee 11:00 – 11:45 Towards an artificial muse for new ideas in science
Mario Krenn, Erlangen
11:05–11:50 Occam’s razor in machine learning
Tom Sterkenburg, Munich
11:45 – 12:15 Navigating the black box: Understanding particle physics with deep neural networks and explainable artifical intelligence
Frauke Stoll, Dortmund
11:50–12:20 A new pathway: From objectual to explanatory understanding with AlphaFold2
Annika Schuster, Dortmund
12:15–13:15 Lunch
12:20–13:20 Lunch 13:15 – 14:00 What can we learn from and through machine learning if the physics of many-body systems is behind it?
Miriam Klopotek, Stuttgart
13:20–14:05 Is knowledge forever? An astronomical perspective
Dominik Elsässer, Dortmund
14:00 – 14:45 Stakes and understanding the decisions of artificial intelligent systems
Eva Schmidt, Dortmund
14:05–14:50 ML-driven knowledge gain in physics
Wolfgang Rhode, Dortmund
14:45 – 15:30 A hypothesis-centric perspective on machine learning in biomedicine
Axel Mosig, Bochum
14:50–15:35 Data, theories, and machine learning in astroparticle physics
Brigitte Falkenburg, Dortmund
15:30 – 15:45 Coffee
15:35–15:50 Coffee 15:45 – 16:30 (to be announced)
Kathleen Creel, Boston
15:50–16:35 Opacity as a stepping stone
Lena Kästner, Bayreuth
16:30 – 17:15 Deep learning for scientific discovery and the theory-freedom-robustness trade-off
Florian Boge, Michael Krämer, Christian Zeitnitz, Dortmund / Aachen / Wuppertal
16:35-17:20 Explainable machine learning in drug discovery
Jürgen Bajorath, Bonn
17:15 – 17:20 Closing Words (FJB)
18:30 – 19:15 Dortmunder U

20:00 – Dinner

Main Organizers

Annika Schuster, Frauke Stoll, and Florian J. Boge