Transparency & Explainability in higher levels of automation in the ATM domain

This paper presents findings, lessons learnt and guidelines for the use of explainable and transparent Artificial Intelligence (AI)/Machine Learning (ML) in ATM. The paper focuses on the results obtained from validating two AI/ML prototypes for Conflict Detection & Resolution (CD&R) and Air Traffic Flow and Capacity Management (ATFCM) problems. These two prototypes are representative of the type of advanced automated systems that can support respectively the tactical and the pre-tactical operational phases The aim is, shifting the paradigm of human-AI teaming, providing full explainability and operational transparency. The major question is: when and how explanations should be provided for systems to be acceptable and trustworthy by operators?