Securing Autonomous Air Traffic Management: Blockchain Networks Driven by Explainable AI

Data fragmentation is a critical safety problem: Current air traffic management systems are "fragmented, siloed, and not easily accessible," creating risks of data conflicts and reduced trust in accuracy—issues that become more dangerous as aviation moves toward autonomous systems that depend on reliable shared data.

Blockchain can enable trustworthy data sharing between competitors: The paper proposes using blockchain networks to create secure, transparent data chains among competing aviation stakeholders, addressing the current lack of trust in data provenance and helping ensure that autonomous traffic management systems receive verified information.

Explainable AI is essential for human oversight: The authors emphasize that explainable AI (rather than "black box" AI) is necessary to "build trust with human stakeholders" in autonomous aviation systems, allowing operators to understand why AI systems make critical safety decisions rather than simply accepting them.

Multiple technologies must work together: The paper argues that blockchain, distributed AI networks, and explainable AI must be "connected together" as an integrated system—no single technology alone can adequately address the security and trust challenges of autonomous air traffic management.

This remains an emerging field requiring significant development: The authors conclude that "significant research and development" is still needed before this approach can reliably enable autonomous air mobility, indicating that practical implementation of these systems for real-world aviation is not yet mature.