论文标题
旨在认证机器学习系统，以进行低临界空气传播应用
Toward Certification of Machine-Learning Systems for Low Criticality Airborne Applications
论文作者
论文摘要
近年来，机器学习领域（ML）的出色进步引起了人们对在航空中使用这项技术的极大兴趣。 ML的可能空气传播应用包括安全至关重要的功能，必须按照航空业的严格认证标准来开发。在ML文艺复兴时期之前，未考虑ML技术的具体情况，在ML复兴之前制定了当前的认证标准。传统设计保证方法与基于ML的系统的某些方面之间存在一些基本不相容性。在本文中，我们分析了当前的机载认证标准，并表明，如果应用了有关ML开发工作流程的某些假设，则可以实现低临界性ML系统的所有目标。
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.
