Sanchez Hernandez, CarolinaAyo, SamuelPanagiotakopoulos, Dimitrios2021-12-202021-12-202021-11-15Sanchez Hernandez C, Ayo S, Panagiotakopoulos D. (2021) An explainable artificial intelligence (xAI) framework for improving trust in automated ATM tools. In: 2021 AIAA/IEEE 40th Digital Avionics Systems Conference (DASC), 3-7 October 2021, San Antonio, USA2155-7209https://doi.org/10.1109/DASC52595.2021.9594341https://dspace.lib.cranfield.ac.uk/handle/1826/17344With the increased use of intelligent Decision Support Tools in Air Traffic Management (ATM) and inclusion of non-traditional entities, regulators and end users need assurance that new technologies such as Artificial Intelligence (AI) and Machine Learning (ML) are trustworthy and safe. Although there is a wide amount of research on the technologies themselves, there seem to be a gap between research projects and practical implementation due to different regulatory and practical challenges including the need for transparency and explainability of solutions. In order to help address these challenges, a novel framework to enable trust on AI-based automated solutions is presented based on current guidelines and end user feedback. Finally, recommendations are provided to bridge the gap between research and implementation of AI and ML-based solutions using our framework as a mechanism to aid advances of AI technology within ATM.enAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/Air Traffic ManagementArtificial IntelligenceMachine LearningTrust FrameworkConference paper978-1-6654-3420-1