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Overview of Initiatives Suitable for Learning Assurance of AI-Based Military Products and Identified Challenges based on Use Cases Analysis

Year: 2025

Type: article

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.

Source: Deutsche Gesellschaft für Luft- und Raumfahrt

DOI: 10.25967/650388

Authors: de Cacqueray, A., Ribas de Amaral, J., Capdevila Llompart, C.M.

Language: English

Topic: Safety Systems Engineering in Autonomy

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7116966448

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