Usage of more transparent and explainable conflict resolution algorithm: air traffic controller feedback

Christophe Hurter (1) , Augustin Degas (1) , Arnaud Guibert (1) , Nicolas Durand (1, 2) , Ana Ferreira (3, 4) , Nicola Cavagnetto (4) , Mir Riyanul Islam (5) , Shaibal Barua (5) , Mobyen Uddin Ahmed (5) , Shahina Begum (5) , Stefano Bonelli (4) , Giulia Cartocci (4) , Gianluca Di Flumeri (6) , Gianluca Borghini (6) , Fabio Babiloni (6) , Pietro Aricó (6)

1 ENAC - Ecole Nationale de l'Aviation Civile
2 IRIT-APO - Algorithmes Parallèles et Optimisation
3 Universidade de Aveiro
4 Deep Blue Srl ,Roma, Italie
5 MDH - Mälardalen University
6 UNIROMA - Università degli Studi di Roma "La Sapienza" = Sapienza University [Rome]

Recently, Artificial intelligence (AI) algorithms have received 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 algorithms with extended transparency.

Keywords: Air Traffic Control; Explainable AI; Algorithm Transparency; Genetic Algorithm

Domains: Artificial Intelligence [cs.AI]

Christophe Hurter, Augustin Degas, Arnaud Guibert, Nicolas Durand, Ana Ferreira, et al.. Usage of more transparent and explainable conflict resolution algorithm: air traffic controller feedback. Transportation Research Procedia, 2022, 66 : 34th Conference of the European Association for Aviation Psychology, pp.270-278. ⟨10.1016/j.trpro.2022.12.027⟩. ⟨hal-04008704⟩