Abstract
Flight Take-Off Time (TOT) delay prediction is essential to optimizing capacity-related tasks in Air Traffic Management (ATM) systems. Recently, the ATM domain has put afforded to predict TOT delays using machine learning (ML) algorithms, often seen as “black boxes”, therefore it is difficult for air traffic controllers (ATCOs) to understand how the algorithms have made this decision. Hence, the ATCOs are reluctant to trust the decisions or predictions provided by the algorithms. This research paper explores the use of explainable artificial intelligence (XAI) in explaining flight TOT delay to ATCOs predicted by ML-based predictive models. Here, three post hoc explanation methods are employed to explain the models’ predictions. Quantitative and user evaluations are conducted to assess the acceptability and usability of the XAI methods in explaining the predictions to ATCOs. The results show that the post hoc methods can successfully mimic the inference mechanism and explain the models’ individual predictions. The user evaluation reveals that user-centric explanation is more usable and preferred by ATCOs. These findings demonstrate the potential of XAI to improve the transparency and interpretability of ML models in the ATM domain.
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Acknowledgements
This work was financed by the European Union’s Horizon 2020 within the framework SESAR 2020 research and innovation program under grant agreement N. 894238, project Transparent Artificial Intelligence and Automation to Air Traffic Management Systems (ARTIMATION) and BrainSafeDrive, co-funded by the Vetenskapsrådet - The Swedish Research Council and the Ministero dell’Istruzione dell’Università e della Ricerca della Repubblica Italiana under Italy-Sweden Cooperation Program.
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Jmoona, W. et al. (2023). Explaining the Unexplainable: Role of XAI for Flight Take-Off Time Delay Prediction. In: Maglogiannis, I., Iliadis, L., MacIntyre, J., Dominguez, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 676. Springer, Cham. https://doi.org/10.1007/978-3-031-34107-6_7
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