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Autor(en):
A. de Cacqueray, J. Ribas de Amaral, C.M. Capdevila Llompart
Zusammenfassung:
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.
Veranstaltung:
Deutscher Luft- und Raumfahrtkongress 2025, Augsburg
Verlag, Ort:
Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V., Bonn, 2025
Medientyp:
Conference Paper
Sprache:
englisch
Format:
21,0 x 29,7 cm, 10 Seiten
URN:
urn:nbn:de:101:1-2512221130224.935211878668
DOI:
10.25967/650388
Stichworte zum Inhalt:
Defence and Security, Artificial Intelligence
Verfügbarkeit:
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Kommentar:
Zitierform:
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. Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V.. (Text). https://doi.org/10.25967/650388. urn:nbn:de:101:1-2512221130224.935211878668.
Veröffentlicht am:
22.12.2025