Can I read Traceable Business-to-safety Analysis Framework for Safety-critical Machine Learning Systems on EtoBox?

Traceable Business-to-safety Analysis Framework for Safety-critical Machine Learning Systems by Jati H. Husen; Hironori Washizaki; Hnin Thandar Tun; Nobukazu Yoshioka; Yoshiaki Fukazawa; Hironori Takeuchi is a scholarly article available to read on EtoBox.

What is Traceable Business-to-safety Analysis Framework for Safety-critical Machine Learning Systems about?

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.

Author

Jati H. Husen; Hironori Washizaki; Hnin Thandar Tun; Nobukazu Yoshioka; Yoshiaki Fukazawa; Hironori Takeuchi

Publisher

ACM

Published

2022

Language

EN