机器学习系统认证的形式化与实践要素
Formal and Practical Elements for the Certification of Machine Learning Systems
AI总结：
针对航空航天领域机器学习模型难以适配传统自底向上认证模式的问题，本文结合形式化理论保证与实践考量，提出基于可扩展统计验证器的模型无关、工具独立的安全关键ML系统认证框架，并在视觉着陆任务上验证了有效性。
摘要：
过去十年间，机器学习展现出令人瞩目的成果，在与自主飞行相关的感知任务中，其能力往往超越人类。与传统航空航天软件不同，机器学习模型的参数并非人工编码，也不源于物理规律，而是从数据中学习得到的。这些参数在训练阶段自动调整，其取值通常与物理要求不对应。因此，需求无法直接追溯到代码行，这对当前自底向上的航空航天认证模式造成了阻碍。本文试图通过三方面弥补这一缺口：1）阐明构建机器学习模型的内部运作机制与流程；2）形式化确立这些流程所能提供的理论保证；3）用实践考量补充这些形式化要素，为安全关键型机器学习系统构建完整的认证论证。我们提出的框架基于可扩展的统计验证器，具备模型无关性与工具独立性，可适配工业界的众多用例。我们在自主飞行的一项广泛应用——基于视觉的着陆——上展示了实验结果。
英文摘要：
Over the past decade, machine learning has demonstrated impressive results, often surpassing human capabilities in sensing tasks relevant to autonomous flight. Unlike traditional aerospace software, the parameters of machine learning models are not hand-coded nor derived from physics but learned from data. They are automatically adjusted during a training phase, and their values do not usually correspond to physical requirements. As a result, requirements cannot be directly traced to lines of code, hindering the current bottom-up aerospace certification paradigm. This paper attempts to address this gap by 1) demystifying the inner workings and processes to build machine learning models, 2) formally establishing theoretical guarantees given by those processes, and 3) complementing these formal elements with practical considerations to develop a complete certification argument for safety-critical machine learning systems. Based on a scalable statistical verifier, our proposed framework is model-agnostic and tool-independent, making it adaptable to many use cases in the industry. We demonstrate results on a widespread application in autonomous flight: vision-based landing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
