The European Union Aviation Safety Agency (EASA) is developing guidelines to certify AI-based systems in aviation with learning assurance as a key framework. Central to the learning assurance are the definitions of a Concept of Operations, an Operational Domain, and an AI/ML constituent Operational Design Domain (ODD). However, because no further guidance on these concepts is provided to developers, this work introduces a framework for defining them. For the concepts of the Operational Domain of the overall system and the AI/ML constituent ODD, a tabular definition language is introduced. Furthermore, processes are introduced to define the different necessary artifacts. During the specification process for the AI/ML constituent ODD, existing steps were identified and consolidated, including the identification of domain-specific concepts for the AI/ML constituent. To validate the framework, it was applied to the pyCASX system, which employs neural-network-based compression. For this use case, the framework produced an AI/ML constituent ODD with finer detail than other ODDs defined for the same airborne collision avoidance use case. Thus, the proposed novel framework is an important step toward a holistic approach aligned with EASA’s guidelines.

The European Union Aviation Safety Agency (EASA) is developing guidelines to certify AI-based systems in aviation with learning assurance as a key framework. Central to the learning assurance are the definitions of a Concept of Operations, an Operational Domain, and an AI/ML constituent Operational Design Domain (ODD). However, since no further guidance for these concepts is provided to developers, this work introduces a methodology for their definition. Concerning the concepts of the Operational Domain of the overall system and the AI/ML constituent ODD, a tabular definition language for both is introduced. Furthermore, processes are introduced to define the different necessary artifacts. For the specification process of the AI/ML constituent ODD, different preexisting steps were identified and combined, such as the identification of domain-specific concepts for the AI/ML constituent. To validate the methodology, it was applied to the pyCASX system that utilizes neural network-based compression. For the use case, the methodology proved it was able to produce an AI/ML constituent ODD of finer detail compared to other ODDs defined for the same airborne collision avoidance use case. Thus, the proposed novel framework is an important step toward a holistic framework following EASA’s guidelines.