Bringing an AI-based system to market in commercial aviation is not simply a matter of building technology that works. It requires demonstrating to the Federal Aviation Administration — and, for international deployments, to EASA and other national authorities — that the system meets rigorous standards for safety, reliability, and airworthiness. This process, known as certification, is one of the most demanding in any industry. For AI-based systems, it presents unique challenges that the regulatory community is still working to address.
The Certification Framework
Aircraft modifications that affect airworthiness — including the installation of new sensor systems and their associated software — typically require a Supplemental Type Certificate (STC). An STC is issued by the FAA after the applicant demonstrates that the modification meets applicable airworthiness standards and does not adversely affect the safety of the aircraft.
For hardware-only modifications, the STC process is well-established and relatively predictable. The applicant must demonstrate that the hardware meets structural, electrical, and environmental standards, and that its installation does not compromise the integrity of existing aircraft systems. For software-based systems, and particularly for AI-based systems, the process is more complex.
The Challenge of AI Explainability
Traditional aviation software certification standards — primarily DO-178C for airborne software and DO-254 for airborne electronic hardware — were developed for deterministic systems where the behavior of the software can be fully specified and verified. AI systems, by their nature, are not fully deterministic: their outputs depend on learned parameters that are derived from training data rather than explicitly programmed logic.
This creates a fundamental tension with the explainability requirements that underpin aviation certification. Regulators need to understand not just what a system does, but why it does it — and for a neural network with millions of parameters, providing that explanation is genuinely difficult. The FAA has acknowledged this challenge and is actively developing new guidance for the certification of machine learning-based systems.
Our Approach at Platinum Eagle Aerospace
In developing the certification strategy for the FLEX AI Platform, our team worked closely with FAA certification specialists from the earliest stages of the program. Rather than treating certification as a final hurdle to clear after the technology was developed, we integrated regulatory considerations into the design process from day one.
Key elements of our approach included: extensive validation datasets that demonstrate system performance across a wide range of operational conditions; a hybrid architecture that combines AI-based anomaly detection with deterministic rule-based alerting for safety-critical functions; and a comprehensive monitoring and logging framework that provides the audit trail regulators need to evaluate system behavior.
The Evolving Regulatory Landscape
The FAA's approach to AI certification is evolving rapidly. The agency has published initial guidance on the use of machine learning in aviation systems and is actively engaging with industry to develop more comprehensive standards. We expect the regulatory framework to become significantly clearer over the next two to three years, which will reduce the uncertainty and cost associated with certifying AI-based aviation systems.
For operators and developers working in this space today, the key is to engage early with your certification authority, document your development and validation process rigorously, and be prepared for an iterative dialogue with regulators as the standards continue to evolve.