Formulating a Learning Assurance-Based Framework for AI-Based Systems in Aviation

Friedrich Werner, Johann Maximilian Christensen, Thomas Stefani, Frank Köster, Elena Hoemann, Sven Hallerbach

Abstract: 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.

External IDs:doi:10.20944/preprints202511.0786.v1