arXiv:2605.20607v1 Announce Type: new Abstract: EASA's learning-assurance guidance requires data-driven aviation systems to build and monitor their own situation representation, yet for neural networks the technical means to provide such evidence remain an open problem. We address this gap for a vision-based aircraft landing system: we propose that a minimally assurable model must at least be shown to separate content from style in its own situation representation. Showing that the model's predictions then rely largely on the contentful representation components leads to a concrete assurance p
The increasing deployment of AI in safety-critical systems like autonomous aviation necessitates robust assurance methods, addressed by new regulatory guidance like EASA's.
This development tackles a significant bottleneck for AI adoption in regulated industries by proposing a concrete technical solution for model interpretability and assurance.
The ability to demonstrate 'content-style separation' in AI models could become a standard for certifying neural networks in critical applications, accelerating their integration.
- · AI assurance and verification companies
- · Aviation technology developers
- · Regulated industries adopting AI
- · AI safety researchers
- · AI developers unwilling to adopt interpretability standards
- · Traditional, non-AI based safety systems
It provides a clear technical path for meeting EASA's learning assurance guidelines for vision-based aviation systems.
This framework could generalize to other safety-critical AI applications, accelerating AI deployment in sectors like autonomous driving and medical devices.
It might lead to the development of new AI architectures inherently designed for interpretability and assurance, shifting fundamental AI research focus.
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