  Advances in machine learning (ML) open the way to innovating functions in the
avionic domain, such as navigation/surveillance assistance (e.g. vision-based
navigation, obstacle sensing, virtual sensing), speechto-text applications,
autonomous flight, predictive maintenance or cockpit assistance. Current
certification standards and practices, which were defined and refined decades
over decades with classical programming in mind, do not however support this
new development paradigm. This article provides an overview of the main
challenges raised by the use ML in the demonstration of compliance with
regulation requirements, and a survey of literature relevant to these
challenges, with particular focus on the issues of robustness and
explainability of ML results.
