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This research addresses the critical need for trust in AI by applying explainability techniques to reinforcement learning algorithms within safety-critical air traffic control scenarios. By utilizing saliency maps, the study aims to clarify how agents select flight routes that avoid no-fly zones, facilitating better human-AI collaboration in high-stakes environments. The work represents a significant step toward integrating deep RL into domains where decision transparency is mandatory for operational adoption.
Explainable Reinforcement Learning for assisting Air Traffic Controllers
arXiv:2607.22525v1 Announce Type: new Abstract: To effectively integrate AI into high-stakes, critical environments such as healthcare, autonomous driving, and aviation--and to advance toward higher levels of automation and seamless human-AI collaboration--building trust in AI-driven solutions is essential. Trust, in turn, is closely linked to the explainability of AI systems. The rapid advancements in AI across various domains have underscored the challenges of establishing trust, raising increasing interest in AI explainability even more when applied to deep learning. In this context, the present work aims to explore the application of explainability techniques to Reinforcement Learning (RL) algorithms, specifically within the safety-critical domain of Air Traffic Control (ATC). Using a simplified ATC environment as an initial testbed, an intelligent agent is trained with a reinforcement learning algorithm to make decisions on alternative flight routes that avoid no-fly zones. As a preliminary explainability approach, a saliency map is employed, providing insights into the input features that most significantly influence the agent's decision-making process.
Abstract:To effectively integrate AI into high-stakes, critical environments such as healthcare, autonomous driving, and aviation--and to advance toward higher levels of automation and seamless human-AI collaboration--building trust in AI-driven solutions is essential. Trust, in turn, is closely linked to the explainability of AI systems. The rapid advancements in AI across various domains have underscored the challenges of establishing trust, raising increasing interest in AI explainability even more when applied to deep learning. In this context, the present work aims to explore the application of explainability techniques to Reinforcement Learning (RL) algorithms, specifically within the safety-critical domain of Air Traffic Control (ATC). Using a simplified ATC environment as an initial testbed, an intelligent agent is trained with a reinforcement learning algorithm to make decisions on alternative flight routes that avoid no-fly zones. As a preliminary explainability approach, a saliency map is employed, providing insights into the input features that most significantly influence the agent's decision-making process.
Artificial Intelligence (cs.AI)
arXiv:2607.22525 [cs.AI]
(or arXiv:2607.22525v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.22525 arXiv-issued DOI via DataCite (pending registration)
In: L. Barolli (ed.), Advanced Information Networking and Applications (AINA 2025), Lecture Notes on Data Engineering and Communications Technologies, vol. 250, pp. 148-157, Springer, Cham (2025)
https://doi.org/10.1007/978-3-031-87778-0_14 DOI(s) linking to related resources
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