We consider the use of concept bottleneck models (CBMs) to enhance safety argumentation for classification tasks in safety-critical systems. When constructing a safety argumentation for Machine Learning (ML) models, there exists a semantic gap between the specified behaviour, given through class labels at training time, and the learnt behaviour, measured through performance metrics. We address this gap by using CBMs, a class of interpretable ML models in which the predictions rely on a set of human-defined concepts. A Goal Structuring Notation (GSN)-based safety assurance case is constructed including such concepts, allowing traceability between the system specification and the behaviour of the model. As a result, a line of safety argumentation is provided that relies on the interpretable model trained to satisfy the specified safety requirements.

We consider the problem of achieving a required level of confidence about safety-critical systems consisting of interacting components. Especially, we address restrictions in traditional A/G reasoning techniques which may cause false positives in contract compatibility analyses. Therefore, we introduce interface assertions, i. e., predicate logical formulae over the components' interfaces. We show how to compute interface assertions for architecture configurations based on the interface assertions of the corresponding components and show soundness and relative completeness of the method. Moreover, we introduce mode-based contracts, which - as a special kind of interface assertions - consist of dedicated assume and guarantee pairs. They provide a methodological guidance for developers and facilitate contract specification in contrast to e. g. traditional A/G reasoning. For this concept, we provide algorithms to check under-specification, over-specification, and the fulfillment of specifications. We also sketch how the checks can be operationalized using SMT solvers. Finally, an example demonstrates the approach.