  The use of Air traffic management (ATM) simulators for planing and operations
can be challenging due to their modelling complexity. This paper presents XALM
(eXplainable Active Learning Metamodel), a three-step framework integrating
active learning and SHAP (SHapley Additive exPlanations) values into simulation
metamodels for supporting ATM decision-making. XALM efficiently uncovers hidden
relationships among input and output variables in ATM simulators, those usually
of interest in policy analysis. Our experiments show XALM's predictive
performance comparable to the XGBoost metamodel with fewer simulations.
Additionally, XALM exhibits superior explanatory capabilities compared to
non-active learning metamodels.
  Using the `Mercury' (flight and passenger) ATM simulator, XALM is applied to
a real-world scenario in Paris Charles de Gaulle airport, extending an arrival
manager's range and scope by analysing six variables. This case study
illustrates XALM's effectiveness in enhancing simulation interpretability and
understanding variable interactions. By addressing computational challenges and
improving explainability, XALM complements traditional simulation-based
analyses.
  Lastly, we discuss two practical approaches for reducing the computational
burden of the metamodelling further: we introduce a stopping criterion for
active learning based on the inherent uncertainty of the metamodel, and we show
how the simulations used for the metamodel can be reused across key performance
indicators, thus decreasing the overall number of simulations needed.
