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Towards certifiable AI in aviation: landscape, challenges, and opportunities

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arxiv 2409.08666 v1 pith:JUBLXKQJ submitted 2024-09-13 cs.LG cs.AI

classification cs.LGcs.AI
keywords avionicscertificationchallengesmethodsacceptableachieveaddressartificial
verification ladder T0 review T1 audit T2 compute T3 formal
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Artificial Intelligence (AI) methods are powerful tools for various domains, including critical fields such as avionics, where certification is required to achieve and maintain an acceptable level of safety. General solutions for safety-critical systems must address three main questions: Is it suitable? What drives the system's decisions? Is it robust to errors/attacks? This is more complex in AI than in traditional methods. In this context, this paper presents a comprehensive mind map of formal AI certification in avionics. It highlights the challenges of certifying AI development with an example to emphasize the need for qualification beyond performance metrics.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Approach Towards Semi-Automated Certification for Low Criticality ML-Enabled Airborne Applications

    cs.SE 2025-01 reject novelty 5.0 of 10

    A semi-automated certification framework for DO-178C Level D ML systems is demonstrated on a YOLOv8 vehicle detector, producing a Moderate Assurance score of 74.7.

  2. Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces

    cs.LG 2024-12 conditional novelty 4.0 of 10

    A weighted sum of cross-entropy and a PCA-condensed, per-cluster-variance Magnet loss with dynamic alpha and beta schedules improves accuracy and latent cluster quality on three image benchmarks.

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