Formal and Practical Elements for the Certification of Machine Learning
  Systems
Jean-Guillaume Durand Arthur Dubois Robert J. Moss
Over the past decade, machine learning has demonstrated impressive results,
often surpassing human capabilities in sensing tasks relevant to autonomous
flight. Unlike traditional aerospace software, the parameters of machine
learning models are not hand-coded nor derived from physics but learned from
data. They are automatically adjusted during a training phase, and their values
do not usually correspond to physical requirements. As a result, requirements
cannot be directly traced to lines of code, hindering the current bottom-up
aerospace certification paradigm. This paper attempts to address this gap by 1)
demystifying the inner workings and processes to build machine learning models,
2) formally establishing theoretical guarantees given by those processes, and
3) complementing these formal elements with practical considerations to develop
a complete certification argument for safety-critical machine learning systems.
Based on a scalable statistical verifier, our proposed framework is
model-agnostic and tool-independent, making it adaptable to many use cases in
the industry. We demonstrate results on a widespread application in autonomous
flight: vision-based landing.