A Safety Case Pattern for Systems with Machine Learning Components

Ernest Wozniak, Carmen Cârlan, Esra Acar-Celik und Henrik J. Putzer

Proceedings of the International Conference on Computer Safety, Reliability, and Security (SAFECOMP), pp. 370–382

September 2020 · DOI:10.1007/978-3-030-55583-2_28

Zusammenfassung

Several standards from the domain of safety critical systems, in order to support the argumentation of the safety assurance of a system under development, recommend the construction of a safety case. This activity is guided by the objectives to be met, recommended or required by the standards along the safety lifecycle. Ongoing attempts to use Machine Learning (ML) for safety critical functionality revealed certain deficits. For instance, the widely recognized standard for functional safety of automotive systems, ISO 26262, which can be used as a basis to construct a safety case, does not reason about ML. To this end, the goal of this work is to provide a pattern for arguing about the correct implementation of safety requirements in system components based on ML. The pattern is integrated within an overall encompassing approach for safety case generation for automotive systems and its applicability is showcased on a pedestrian avoidance system.