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

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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022
出版社Institute of Electrical and Electronics Engineers Inc.
ページ50-51
ページ数2
ISBN(電子版)9781450392754
DOI
出版ステータスPublished - 2022
イベント1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022 - Pittsburgh, United States
継続期間: 2022 5月 162022 5月 17

出版物シリーズ

名前Proceedings - 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022

Conference

Conference1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022
国/地域United States
CityPittsburgh
Period22/5/1622/5/17

ASJC Scopus subject areas

  • 人工知能
  • ソフトウェア

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