Toward a more transparent and explainable conflict resolution algorithm for air traffic controllers

Recently, Artificial intelligence (AI) algorithms have shown increasable interest in various application domains including in Air
Transportation Management (ATM). Different AI in particular Machine Learning (ML) algorithms are used to provide decision
support in autonomous decision-making tasks in the ATM domain e.g., predicting air transportation traffic and optimizing traffic
flows. However, most of the time these automated systems are not accepted or trusted by the intended users as the decisions
provided by AI are often opaque, non-intuitive and not understandable by human operators. Safety is the major pillar to air traffic
management, and no black box process can be inserted in a decision-making process when human life is involved. To address this
challenge related to transparency of the automated system in the ATM domain, we investigated AI methods in predicting air
transportation traffic conflict and optimizing traffic flows based on the domain of Explainable Artificial Intelligence (XAI). Here,
AI models’ explainability in terms of understanding a decision i.e., post hoc interpretability and understanding how the model
works i.e., transparency can be provided for air traffic controllers. In this paper, we report our research directions and our findings
to support better decision making with AI algorithm with extended transparency.