  The exceptional progress in the field of machine learning (ML) in recent
years has attracted a lot of interest in using this technology in aviation.
Possible airborne applications of ML include safety-critical functions, which
must be developed in compliance with rigorous certification standards of the
aviation industry. Current certification standards for the aviation industry
were developed prior to the ML renaissance without taking specifics of ML
technology into account. There are some fundamental incompatibilities between
traditional design assurance approaches and certain aspects of ML-based
systems. In this paper, we analyze the current airborne certification standards
and show that all objectives of the standards can be achieved for a
low-criticality ML-based system if certain assumptions about ML development
workflow are applied.
