AI Aviation Evaluation Working Group

    Shaping the Future of Aviation AI Safety

    The AI Aviation Evaluation Working Group is a collaborative initiative bringing together aviation industry experts, regulatory bodies, AI specialists, and safety professionals to develop comprehensive evaluation frameworks for AI systems in aviation.

    As AI adoption accelerates across the aviation sector—from operations to maintenance, from customer service to flight planning—the need for rigorous, aviation-specific evaluation standards has never been more critical. Our working group addresses this challenge head-on.

    Our Mission

    Develop aviation-specific AI evaluation methodologies that ensure safety, reliability, and regulatory compliance while enabling innovation. We create practical testing frameworks that address the unique challenges of aviation AI deployment.

    Why Aviation Needs Specialized Standards

    Aviation's safety-critical environment, complex regulatory landscape, and specialized terminology require evaluation frameworks that go beyond generic AI testing. We develop standards that understand aviation's unique requirements.

    Key Focus Areas

    Safety-Critical Decision Making

    Evaluation methodologies for AI systems that influence safety-critical aviation decisions, including emergency procedures, operational limitations, and risk assessment capabilities.

    Regulatory Compliance Testing

    Frameworks for validating AI alignment with CAA, EASA, FAA, and ICAO requirements, including operational approvals, certification standards, and continuing airworthiness obligations.

    Domain Knowledge Validation

    Comprehensive testing of AI understanding of aviation terminology, procedures, regulations, and operational context across ground operations, flight operations, maintenance, and air traffic management.

    Human Factors Integration

    Evaluation of AI systems' interaction with human operators, including situational awareness support, workload management, automation bias mitigation, and crew resource management considerations.

    Adversarial Robustness

    Testing frameworks for identifying vulnerabilities to adversarial attacks, including prompt injection, data poisoning, and model manipulation that could compromise aviation safety or security.

    Performance Under Degraded Conditions

    Evaluation of AI system behavior under non-normal conditions, including partial data availability, system failures, emergency scenarios, and edge cases that aviation operations frequently encounter.

    Who Should Join

    Aviation Organizations

    • • Airlines and operators deploying AI systems
    • • Ground handling service providers
    • • Maintenance, repair, and overhaul organizations
    • • Air navigation service providers
    • • Airport operators

    Regulatory & Standards Bodies

    • • Civil aviation authorities
    • • International standards organizations
    • • Safety investigation agencies
    • • Industry associations

    Technology Providers

    • • AI/ML technology companies
    • • Aviation software developers
    • • Safety management system providers
    • • Aviation data analytics platforms

    Academic & Research

    • • University aviation programs
    • • AI safety research groups
    • • Human factors specialists
    • • Aviation safety researchers

    Collaborative Approach

    The working group operates through regular virtual meetings, collaborative document development, public consultation periods, and industry workshops. We believe that effective aviation AI standards can only be developed through genuine collaboration between all stakeholders.

    Our outputs include evaluation frameworks, test datasets, compliance checklists, best practice guidance, and case studies. All materials are developed openly with input from the aviation community and published for industry adoption.

    Join the Working Group

    Help shape the future of aviation AI safety. Whether you represent an airline, regulator, technology provider, or academic institution, your expertise is valuable to developing effective evaluation standards.

    Express Interest in Joining