Explore the gaps between AI’s black-box behavior and aerospace’s demand for deterministic processes. Learn how EASA is tackling these challenges through its W-shaped assurance process.
Discover how to model edge cases, handle non-deterministic behavior, and ensure your AI systems meet latency, accuracy, and explainability benchmarks.
Understand how to adapt traditional DO-178C testing methods like fault injection and back-to-back comparison to AI models with non-linear outputs and complex decision pathways.
Safety doesn’t end at deployment. Learn how to monitor, retrain, and recalibrate AI systems over time while preserving safety integrity and regulatory compliance.
Trust but verify. This paper outlines how to ensure human oversight in AI operations, enabling safe interventions when systems reach the boundaries of their capabilities.
Refine your safety processes and adapt to future airworthiness requirements. This guide will help you align ML development with established frameworks like DO-178C and DO-278A.
Understand how emerging standards affect your certification strategy. Get clarity on the EASA AI Roadmap, lifecycle assurance, and model traceability.
Gain insights into model selection, feature engineering, and robust validation techniques—especially for edge cases and rare scenarios in real-world flight conditions.
If you’re working on drones, flying taxis, or autonomous aircraft, this white paper offers practical strategies to safely integrate AI into flight-critical systems.
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