A. Monzon Diaz, C.M. Capdevila Llompart

In recent years, civil regulatory agencies, particularly the European Union Aviation Safety Agency (EASA), have made significant effort in establishing a specific regulatory framework to certify Machine Learning (ML) technologies in aviation. However, in the military sector, few initiatives have been launched, such as the NATO AI Strategy and the European Defence Agency´s Trustworthiness for AI in Defence Working Group. Certifying ML applications for military aviation presents unique challenges and risks that require tailored approaches and solutions to ensure safety and operational effectiveness. The purpose of this paper is to identify and discuss the particularities of certifying systems including ML-based technology for military application. In particular the following aspects will be discussed: relevance of tactical mission, adaptability and flexibility, resilience, data availability and ethical considerations for military applications. A case study of a military system that integrates an ML component to assist the Air Refueling Operator (ARO) will be presented. This case study is intended to highlight the importance of transferring civil best practices to the military in order to harmonize standards and achieve mutual recognition on both sides and to identify gaps where civil guidelines cannot cover military aspects. Finally, this paper will propose an outlook of anticipated military regulatory framework which could be used to certify applications embedding AI/ML technology in military aviation in the future.

Deutscher Luft- und Raumfahrtkongress 2024, Hamburg

Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V., Bonn, 2024

Conference Paper

englisch

21,0 x 29,7 cm, 10 Seiten

urn:nbn:de:101:1-2410181434069.483440524112

10.25967/630093

Artificial Intelligence, Airworthiness

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Monzon Diaz, A.; Capdevila Llompart, C.M. (2024): Particularities of Certifying Artificial Intelligence in Military Aviation. Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V.. (Text). https://doi.org/10.25967/630093. urn:nbn:de:101:1-2410181434069.483440524112.

18.10.2024