Costa de Araujo, João PauloBalu, Balahari VigneshReichmann, EikKelly, JessicaKuegele, StefanMata, NúriaGrunske, LarsKoziolek, AnneLamprecht, Anna-LenaThüm, ThomasBurger, Erik2025-02-142025-02-1420252944-7682https://dl.gi.de/handle/20.500.12116/45778In this extended abstract we summarize our work on using Concept Bottleneck Models (CBMs) for an enhanced safety argumentation of vision-based Machine Learning (ML) perception components in safety critical systems. This paper has been published at the International Symposium on Software Reliability Engineering (ISRRE’24)enConcept Bottleneck ModelsSafety ArgumentationImage ClassificationAutonomous Driving10.18420/se2025-182944-7682