We propose a method for deploying a safety-critical machine-learning component into continuously evolving environments where an increased degree of automation in the engineering process is desired. We associate semantic tags with the safety case argumentation and turn each piece of evidence into a quantitative metric or a logic formula. With proper tool support, the impact can be characterized by a query over the safety argumentation tree to highlight evidence turning invalid. The concept is exemplified using a vision-based emergency braking system of an autonomous guided vehicle for factory automation.
@article{arxiv.2209.11632,
title = {Facilitating Change Implementation for Continuous ML-Safety Assurance},
author = {Chih-Hong Cheng and Nguyen Anh Vu Doan and Balahari Balu and Franziska Schwaiger and Emmanouil Seferis and Simon Burton and Yassine Qamsane and Ankit Shukla and Yinchong Yang and Zhiliang Wu and Andreas Hapfelmeier and Ingo Thon},
journal= {arXiv preprint arXiv:2209.11632},
year = {2022}
}