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

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, 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.