The current certification process for aerospace software is not adapted to "AI-based" algorithms such as deep neural networks. Unlike traditional aerospace software, the precise parameters optimized during neural network training are as important as (or more than) the code processing the network and they are not directly mathematically understandable. Despite their lack of explainability such algorithms are appealing because for some applications they can exhibit high performance unattainable with any traditional explicit line-by-line software methods. This paper proposes a framework and principles that could be used to establish certification methods for neural network models for which the current certification processes such as DO-178 cannot be applied. While it is not a magic recipe, it is a set of common sense steps that will allow the applicant and the regulator increase their confidence in the developed software, by demonstrating the capabilities to bring together, trace, and track the requirements, data, software, training process, and test results.

翻译：当前航空航天软件的认证流程不适用于基于深度神经网络等"人工智能"算法。与传统航空航天软件不同，神经网络训练过程中优化的精确参数与处理网络的代码同等重要（甚至更重要），且这些参数无法直接通过数学方式理解。尽管缺乏可解释性，这类算法仍具有吸引力，因为在某些应用中，它们能展现出传统逐行显式软件方法无法企及的高性能。本文提出一个可用于建立神经网络模型认证方法的框架与原则，这类模型无法适用现有如DO-178等认证流程。虽非万能方案，但本框架提供一套基本且合理的步骤，通过展示整合、追溯及追踪需求、数据、软件、训练过程与测试结果的能力，使申请方与监管方能够增强对所开发软件的信心。