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Over the past decade, machine learning has demonstrated impressive results,often surpassing human capabilities in sensing tasks relevant to autonomousflight. Unlike traditional aerospace software, the parameters of machinelearning models are not hand-coded nor derived from physics but learned fromdata. They are automatically adjusted during a training phase, and their valuesdo not usually correspond to physical requirements. As a result, requirementscannot be directly traced to lines of code, hindering the current bottom-upaerospace 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, and3) complementing these formal elements with practical considerations to developa complete certification argument for safety-critical machine learning systems.Based on a scalable statistical verifier, our proposed framework ismodel-agnostic and tool-independent, making it adaptable to many use cases inthe industry. We demonstrate results on a widespread application in autonomousflight: vision-based landing.

Formal and Practical Elements for the Certification of Machine Learning Systems