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Traceable Business-to-Safety Analysis Framework for Safety-critical Machine Learning Systems

J. H. Husen; H. Washizaki; H. T. Tun; N. Yoshioka; Y. Fukazawa; H. Takeuchi

Machine learning-based system requires specific attention towards their safety characteristics while considering the higher-level requirements. This study describes our approach for analyzing machine learning safety requirements top-down from higher-level business requirements, functional requirements, and risks to be mitigated. Our approach utilizes six different modeling techniques: AI Project Canvas, Machine Learning Canvas, KAOS Goal Modeling, UML Components Diagram, STAMP/STPA, and Safety Case Analysis. As a case study, we also demonstrated our approach for lane and other vehicle detection functions of self-driving cars.

Added 2026-04-21