The objective for the project is the exploration of highly automated XAI scenarios through validation activities and Visual Analytics (VA), in order to identify needs and strategies to address transparency and explainability in the operational cases considered, paving the way for the application of these AI/ML technologies in ATM environments.

The proposed research will advance the state-of-the-art in two main areas:

1. Identification of principles and criteria for AI/ML transparency/explainability in ATM domain scenarios, based on the two operational cases considered and with the target to identify transparency requirements for AI/ML methods in general, limiting domain-specific results. This will be achieved by addressing different temporal, functional and safety-critical perspectives, as those provided by the complementary operational cases considered in TAPAS. It is the ambition of the project to maximise the applicability of results to different operational environments, while setting the limitations when this is not feasible.

2. Selection and development of suitable and explainable AI/ML methods in the operational cases identified, to fit the needs of transparency as expressed in the explainability criteria developed for each automation level and according to actors’ needs.
The project will develop prototypes of XAI methods which address the balance between explainability and effectiveness according to specific needs, but also in search of developing a more general taxonomy of AI/ML techniques considering the two aforementioned magnitudes.