Explainable AI (XAI) in Air Traffic Monitoring Systems

Explainable artificial intelligence (XAI) is revolutionizing the field of air traffic monitoring systems by enhancing transparency, trust, and decision-making in complex, high-stakes environments. Traditional artificial intelligence (AI) models often function as “black boxes,” delivering accurate predictions and classifications without providing insight into the rationale behind their outputs. In the domain of air traffic monitoring, where safety and precision are paramount, such opacity poses significant challenges. This chapter explores the integration of XAI techniques into air traffic systems, focusing on their potential to make AI-driven insights interpretable and actionable for air traffic controllers and stakeholders. The chapter begins by examining the unique demands of air traffic monitoring, including real-time decision-making, risk assessment, and coordination among diverse aviation actors. It delves into key XAI methodologies, such as feature attribution, model-agnostic interpretability, and counterfactual explanations, demonstrating their applicability to air traffic scenarios like conflict detection, anomaly identification, and trajectory prediction. Furthermore, case studies illustrate how XAI-enhanced systems can foster collaboration between human operators and AI tools, improving situational awareness and reducing operational errors. Challenges such as scalability, real-time processing, and balancing interpretability with performance are critically discussed, along with potential solutions and future research directions. By elucidating the role of XAI in air traffic monitoring, this chapter aims to underscore its importance in building safer, more efficient aviation ecosystems while maintaining human oversight and accountability.