Representative Signatures in Data Analysis - Epistemological Reflections

Session

Deep learning and scientific realism

16 Oct 2026, 11:45

JvF25/3-303 - Conference Room (Lamarr/RC Trust Dortmund)

9. The Prospects for Realism in AI-Driven Science: The Case of Exoplanet Detection

Dr Mahdi Khalili (Institute of Philosophy, University of Bern)

16/10/2026, 11:45

In this talk, I ask whether and how the use of machine learning (ML) in contemporary science affects the scientific realism debate. After discussing the current state of the debate, I focus on the case of exoplanet detection, where ML has recently been used to distinguish transit signals produced by planets from false positives. I ask, first, whether ML can provide a methodologically distinct...

3. What can we learn from hallucination? Scientific realism and models in astrophysics in the age of the AI

Dr Silvia De Bianchi (Department of Philosophy, Università degli Studi di Milano)

16/10/2026, 14:00

In recent work, Constantin et al. (2026) suggested the development of language models that can generate coherent mathematical representations, advancing the automation of physical law discovery. Thanks to structural insights, they believe that one can inform the training or fine-tuning of language models for symbolic reasoning, encouraging them to generate expressions whose statistical and...

8. The Explanation–Justification Gap in Deep Learning Astroparticle Physics

Dr Koray Karaca (Middle East Technical University Ankara)

16/10/2026, 14:45

Deep learning (DL) models are increasingly used in astroparticle physics for tasks such as gamma–hadron separation, neutrino event reconstruction, and cosmic-ray classification. While these models achieve remarkable predictive accuracy, their opacity poses a challenge to the epistemic standards of discovery. Heatmap-based explainable AI (XAI) techniques —such as heatmaps — promise insight into...

10. Does machine-learning-driven scientific discovery pose new challenges to the No Miracles Argument?

Dr Johannes Mierau (TU Dortmund)

16/10/2026, 15:45

The No Miracles Argument is arguably the strongest case for scientific realism. Yet, it seems that machine-learning-driven scientific discovery evades its justificatory force. Machine learning models are explicitly trained for the purpose of detecting patterns in data, their predictive success comes by design and does not indicate the truth of the underlying representation. Moreover, such...