Title:
Overview of Initiatives Suitable for Learning Assurance of AI-Based Military Products and Identified Challenges based on Use Cases Analysis
Authors:
A. de Cacqueray, J. Ribas de Amaral, C.M. Capdevila Llompart
Abstract:
Machine learning (ML) can enhance military system performance and safety, crucial for future competitiveness. However, this shift from rule-based to data-driven approaches is not covered by current regulations and standards. To ensure reliability and safety, EASA and EUROCAE WG-114 introduced a learning assurance process for civil aeronautical ML-based systems. This paper explores challenges and specificities of applying the proposed learning assurance process for certifying and qualifying military ML-based systems using relevant use cases, focusing on the requirements identification and architecture definition.
Event:
Deutscher Luft- und Raumfahrtkongress 2025, Augsburg
Location, Year:
Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V., Bonn, 2025
Format:
21,0 x 29,7 cm, 10 Pages
Descriptors:
EN: Defence and Security, Artificial Intelligence
Citation:
de Cacqueray, A.; Ribas de Amaral, J.; Capdevila Llompart, C.M. (2025): Overview of Initiatives Suitable for Learning Assurance of AI-Based Military Products and Identified Challenges based on Use Cases Analysis. DGLR Published Conference Papers. Volume 4. (2026). ISSN: 2941-3451. Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V.. (Text). https://doi.org/10.25967/650388. urn:nbn:de:101:1-2512221130224.935211878668.
Publishing Date:
22.12.2025