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Räz, Tim (5 June 2024). Reliability Gaps Between Groups in COMPAS Dataset. In: FAccT '24: The 2024 ACM Conference on Fairness, Accountability, and Transparency. 10.1145/3630106.3658544
Räz, Tim (2024). ML Interpretability: Simple Isn’t Easy. Studies in history and philosophy of science, 103, pp. 159-167. Elsevier 10.1016/j.shpsa.2023.12.007
Räz, Tim (2024). Gerrymandering Individual Fairness. Artificial intelligence, 326, p. 104035. Elsevier 10.1016/j.artint.2023.104035
Räz, Tim (2024). From Explanations to Interpretability and Back (Submitted). In: Philosophy of Science for Machine Learning: Core Issues, New Perspectives. Synthese Library. Springer
Räz, Tim (2023). Methods for identifying emergent concepts in deep neural networks. Patterns, 4(6), p. 100761. Cell Press 10.1016/j.patter.2023.100761
Jebeile, Julie; Lam, Vincent; Majszak, Mason Meyer; Räz, Tim (2023). Machine learning and the quest for objectivity in climate model parameterization. Climatic change, 176(8), p. 101. Springer 10.1007/s10584-023-03532-1
Räz, Tim (2022). Understanding risk with FOTRES? AI & ethics, 3(4), pp. 1153-1167. Springer 10.1007/s43681-022-00223-y
Räz, Tim (2022). COMPAS: zu einer wegweisenden Debatte über algorithmische Risikobeurteilung. Forensische Psychiatrie, Psychologie, Kriminologie, 16(4), pp. 300-306. Springer 10.1007/s11757-022-00741-9
Räz, Tim; Beisbart, Claus (2022). The Importance of Understanding Deep Learning. Erkenntnis - an international journal of analytic philosophy, 89(5), pp. 1823-1840. Springer Netherlands 10.1007/s10670-022-00605-y
Beisbart, Claus; Räz, Tim (2022). Philosophy of science at sea: Clarifying the interpretability of machine learning. Philosophy Compass, 17(6) John Wiley & Sons Ltd 10.1111/phc3.12830
Hertweck, Corinna; Räz, Tim (1 March 2022). Gradual (In) Compatibility of Fairness Criteria (Unpublished). In: AAAI Conference on Artificial Intelligence. Vancouver (Online). March 2022.
Hertweck, Corinna; Räz, Tim (March 2022). Gradual (In)Compatibility of Fairness Criteria (In Press). In: AAAI 2022. Vancouver, Canada. February 22 – March 1, 2022.
Räz, Tim (8 March 2021). Group Fairness: Independence Revisited. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’21) (pp. 129-137). ACM 10.1145/3442188.3445876
Jebeile, Julie; Lam, Vincent; Raez, Tim (21 August 2020). The Impact of Statistics and Machine Learning on Understanding in Climate Modelling (Unpublished). In: Data Science in Climate and Climate Impact Research. Conceptual Issues, Challenges, and Opportunities. ETHZ (online). 20. - 21.08.2020.
Räz, Tim (2020). Understanding Deep Learning With Statistical Relevance (In Press). Philosophy of science : official journal of the Philosophy of Science Association Univ. of Chicago Press
