Artificial Intelligence (AI) has been on the rise in many domains, including numerous safety-critical applications. However, for complex systems found in the real world, or when data already exist, defining the underlying environmental conditions is extremely challenging. This often results in an incomplete description of the environment in which the AI-based system must operate. Nevertheless, this description, called the Operational Design Domain (ODD), is required in many domains for the certification of AI-based systems. Traditionally, the ODD is created in the early stages of the development process, drawing on sophisticated expert knowledge and related standards. This paper presents a novel Safety-by-Design method to a posteriori define the ODD from previously collected data using a multi-dimensional kernel-based representation. This approach is validated through both Monte Carlo methods and a real-world aviation use case for a future safety-critical collision-avoidance system. Moreover, by defining under what conditions two ODDs are equal, the paper shows that the data-driven ODD can equal the original, underlying hidden ODD of the data. Utilizing the novel, Safe-by-Design kernel-based ODD enables future certification of data-driven, safety-critical AI-based systems.

翻译：人工智能（AI）已在众多领域兴起，包括许多安全关键型应用。然而，对于现实世界中的复杂系统，或当数据已存在时，定义其底层环境条件极具挑战性。这通常导致对AI系统必须运行的环境描述不完整。然而，这种被称为运行设计域（ODD）的描述，在许多领域是认证AI系统所必需的。传统上，ODD在开发过程的早期阶段创建，依赖于复杂的专家知识和相关标准。本文提出了一种新颖的"设计即安全"方法，利用基于多维核的表示，从先前收集的数据中后验地定义ODD。该方法通过蒙特卡洛方法和一个未来安全关键防撞系统的真实航空用例进行了验证。此外，通过定义两个ODD在何种条件下相等，本文表明数据驱动的ODD可以等同于数据原始、潜在的隐藏ODD。利用这种新颖的、基于"设计即安全"核的ODD，能够为未来数据驱动的安全关键AI系统提供认证支持。