AIC4ATM

The rapid adoption of Artificial Intelligence in Air Traffic Management (ATM) and airport operations brings real efficiency gains, but also critical challenges around trust, safety, and regulatory compliance. The forthcoming EU AI Act and related frameworks (such as EASA AI guidance) require AI systems in critical infrastructure to be explainable, auditable, and continuously monitored, yet no dedicated aviation-specific mechanism currently exists to do this. AIC4ATM addresses that gap.

A modular AI Compliance Engine

The project is developing a modular AI Compliance Engine that integrates ethical safeguards, operational transparency, Explainable AI, and regulatory adherence for AI applications that are under development or approaching deployment. It combines a top-down analysis of European legal frameworks with bottom-up insights from ATM stakeholders, including AI deployers such as airports, following Responsible Innovation principles of anticipation, inclusion, ethics-by-design, and accountability.

To make compliance concrete, representative AI use cases in ATM and airport operations are identified and clustered, and their technical components decomposed to pinpoint concrete regulatory obligations, building on a Common Taxonomy. These profiles populate a graph-based repository that captures the interrelations among AI attributes, risks, and regulatory requirements, while handling the large data volumes typical of SESAR solutions. A risk taxonomy and compliance logic trees then link identified risks to concrete obligations and support human-machine teaming.

The Compliance Engine design provides end-to-end support: from risk categorization and automated compliance checklists to interfaces that ensure operational clarity and human-centric safeguards such as bias monitoring and continuous oversight. A development roadmap guides the concept through to TRL 7 for real-world deployment, positioning AIC4ATM to foster dependable, data-driven innovation in one of Europe's most safety-critical domains.

Using Sparksee, the CTG supports complex querying, visualization, and pattern detection across highly interconnected datasets, enabling explainability, real-time compliance alerts, and traceability between system components and regulatory frameworks. Large Language Models are layered on top of the graph to enable natural-language querying and to help detect hidden dependencies, turning the CTG into an intelligent compliance assistant.