With 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.


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    Title :

    An Explainable Artificial Intelligence (xAI) Framework for Improving Trust in Automated ATM Tools




    Publication date :

    2021-10-03


    Size :

    3464374 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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