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.

翻译：机器学习(ML)的进步为创新航空领域的功能开辟了道路,如导航/监视协助(例如,基于视觉的导航、障碍感测、虚拟遥感)、语音文字应用、自主飞行、预测性维护或驾驶舱协助等,但数十年来根据传统程序界定和完善的现行认证标准和做法并不支持这一新的发展模式,但这一条概述了使用ML在证明遵守规章要求方面引起的主要挑战,并调查了与这些挑战有关的文献,特别侧重于ML结果的稳健性和可解释性问题。