Applying Concept-Based Models for Enhanced Safety Argumentation

In 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)

Costa de Araujo, João Paulo; Balu, Balahari Vignesh; Reichmann, Eik; Kelly, Jessica; Kuegele, Stefan; Mata, Núria; Grunske, Lars (2025): Applying Concept-Based Models for Enhanced Safety Argumentation. Software Engineering 2025. DOI: 10.18420/se2025-18. Gesellschaft für Informatik, Bonn. ISSN: 2944-7682. EISSN: 2944-7682

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Concept Bottleneck Models , Safety Argumentation , Image Classification , Autonomous Driving

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10.18420/se2025-18

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P360- Software Engineering 2025

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Applying Concept-Based Models for Enhanced Safety Argumentation

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