Feel free to contact me (tim.raez […] posteo.de) for pdfs of published versions.
Preprints
Räz, T.: Transparent and Fair Profiling in Employment Services: Evidence from
Switzerland. [arxiv]
Räz, T.: Authorship and the Politics and Ethics of LLM Watermarks. [arxiv]
Räz, T.: Inter-Rater Reliability is Individual Fairness. [arxiv]
Articles (peer-reviewed journals and conference proceedings)
Räz, T., Pahud de Mortanges, A., Reyes, M. (2025): Explainable AI in medicine: challenges of integrating XAI into the future clinical routine. Frontiers in Radiology, Vol. 5 (2025). [journal] (open access)]
Räz, T. (2024): Reliability Gaps Between Groups in COMPAS Dataset. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT ’24), 113-126. [proceedings] [arxiv]
Räz, T. (2024): ML Interpretability: Simple Isn’t Easy. Studies in History and Philosophy of Science. [journal (open access)][arxiv]
Räz, T. (2024): Gerrymandering Individual Fairness. Artificial Intelligence. [journal (open access)][arxiv]
Jebeile, J., Lam, V., Majszak, M., Räz, T. (2023): Machine learning and the quest for objectivity in climate model parameterization. Climatic Change 176 (101). [journal (open access)]
Räz, T. (2023): Methods for identifying emergent concepts in deep neural networks. Patterns 4 (June 9). [journal (open access)][arxiv]
Räz, T. (2022): COMPAS: zu einer wegweisenden Debatte über algorithmische Risikobeurteilung (COMPAS: on a pathbreaking debate on algorithmic risk assessment). Forensische Psychiatrie, Psychologie, Kriminologie. [journal (open access)]
Räz, T. (2022): Understanding risk with FOTRES? AI and Ethics. [journal (open access)] [philsci-archive]
Räz, T. and C. Beisbart (2022): The Importance of Understanding Deep Learning. Erkenntnis. [journal (open access)] [philsci-archive]
Beisbart, C. and Räz, T. (2022): Philosophy of science at sea: Clarifying the interpretability of machine learning. Philosophy Compass, e12830. [journal (open access)] [philsci-archive]
Hertweck, C. and Räz, T. (2022): Gradual (In)Compatibility of Fairness Criteria. Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI 22), February 22 – March 1, 2022, Vancouver, Canada. [proceedings] [arxiv]
Räz, T. (2022): Understanding Deep Learning With Statistical Relevance. Philosophy of Science 89(1), 20-41. [journal] [philsci-archive]
Räz, T. (2021): Group Fairness: Independence Revisited. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT ’21), March 1–10, 2021, Virtual Event, Canada. [proceedings] [arxiv]
Jebeile, J., Lam, V. and Räz, T. (2021): Understanding Climate Change with Statistical Downscaling and Machine Learning. Synthese 199, 1877-97. [journal] [philsci-archive]
Räz, T. (2018): Euler’s Königsberg: The Explanatory Power of Mathematics. European Journal for Philosophy of Science 8: 331–46. [journal] [philsci-archive]
Räz, T. (2017): The Volterra Principle Generalized. Philosophy of Science 84(4): 737–60. [journal (open access)] [philsci-archive]
Räz, T. (2017): The Silent Hexagon: Explaining Comb Structures. Synthese 194(5): 1703–1724. [journal] [philsci-archive]
Räz, T. and T. Sauer (2015): Outline of a Dynamical Inferential Conception of the Application of Mathematics. Studies in History and Philosophy of Modern Physics 49: 57–72. [journal] [philsci-archive]
Räz, T. (2015): Say My Name: An Objection to Ante Rem Structuralism. Philosophia Mathematica 23(1): 116–25. [journal] [philsci-archive]
Räz, T. (2013): On the Application of the Honeycomb Conjecture to the Bee’s Honeycomb. Philosophia Mathematica 21(3): 351–60. [journal] [philsci-archive]
Scholl, R., and T. Räz (2013): Modeling causal structures. European Journal for Philosophy of Science 3(1): 115–32. [journal] [philsci-archive]
Articles (book chapters, conference proceedings, old preprints)
Räz, T. (2024): From Explanations to Interpretability and Back. Forthcoming in: Juan Durán and Giorgia Pozzi (Eds.): Philosophy of Science for Machine Learning: Core Issues, New Perspectives, Synthese Library. [philsci-archive]
Räz, T. and T. Sauer (2017): The collaboration between Marcel Grossmann and Albert Einstein as a case of the application of mathematics. Proceedings of the Fourteenth Marcel Grossmann Meeting on General Relativity: 3368–3371. [proceedings (open access)]
Räz, T. (2016): The Necessity of Learning for Agency. [philsci-archive]
Räz, T. (2016): Gone Till November: A disagreement in Einstein scholarship. In Sauer, T. and Scholl, R. (eds.): The Philosophy of Historical Case Studies. Boston Studies in Philosophy and History of Science. Springer. [chapter] [philsci-archive]
Scholl, R. and T. Räz (2016): Towards a Methodology for Integrated History and Philosophy of Science. In Sauer, T. and Scholl, R., (eds).: The Philosophy of Historical Case Studies. Boston Studies in Philosophy and History of Science. Springer. [chapter] [philsci-archive]
Räz, T. and T. Sauer (2014): Ein Zyklenmodell der Anwendung von Mathematik. In Ralf Krömer und Gregor Nickel (Hrsg.): Siegener Beiträge zur Geschichte und Philosophie der Mathematik 4. [collection (open access)]
Räz, T. (2013): Comment on “The Undeniable Effectiveness of Mathematics in the Special Sciences”. In M. C. Galavotti, S. Hartmann, M. Weber, W. Gonzalez, D. Dieks, and T. Uebel, eds., New Directions in the Philosophy of Science. Springer. [book]
Other Contributions
Automatisierte Strafjustiz auf wissenschaftlich wackeligen Beinen. Gastbeitrag für algorithmwatch.ch.
Review
Räz, T. (2015): Noson S. Yanofsky (2013): The Outer Limits of Reason. What Science, Mathematics, and Logic Cannot Tell Us. Dialectica. [journal] [philsci-archive]
Thesis
Räz, T. (2014): “On the Applicability of Mathematics – Philosophical and Historical Perspectives”. Ph.D. thesis. University of Lausanne. [pdf]
Tim Räz 