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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 (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 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
