Safe and Explainable AI in 6G Telecommunication Systems: The Way Forward

Artificial Intelligence (AI) is crucial for realizing the evolution of 6 G cellular-based systems. However, the trustworthiness of AI-based models and systems-including their transparency, robustness, human agency, and safety-is equally important for rapid adoption and regulatory compliance. This position paper focuses on the aspects of AI safety and explainability, and on its importance to situation awareness in future networks. We summarize related work, identify research gaps, and provide recommendations for integrating these aspects in a future telecommunication system, such as a 6 G network. We observe that the current state-of-the-art primarily targets modellevel safety and explainability, but argue that there are bigger challenges at the system-level and runtime. As we move towards 6 G networks, there is an urgent need for new research directions to address these challenges. We aim to raise awareness among the research community about these critical issues.

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Published
2025-06-02
DOI
https://doi.org/10.1109/cogsima64436.2025.11079488
Primary Topic
Adversarial Robustness in Machine Learning
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article
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article

Safe and Explainable AI in 6G Telecommunication Systems: The Way Forward

Alexandros Palaios, Rafia Inam, Swarup Kumar Mohalik, Klaus Raizer et al.
Adversarial Robustness in Machine Learning
article

Safe and Explainable AI in 6G Telecommunication Systems: The Way Forward

Alexandros Palaios, Rafia Inam, Swarup Kumar Mohalik, Klaus Raizer, Amadeu Do Nascimento Júnior
article en

Abstract

Artificial Intelligence (AI) is crucial for realizing the evolution of 6 G cellular-based systems. However, the trustworthiness of AI-based models and systems-including their transparency, robustness, human agency, and safety-is equally important for rapid adoption and regulatory compliance. This position paper focuses on the aspects of AI safety and explainability, and on its importance to situation awareness in future networks. We summarize related work, identify research gaps, and provide recommendations for integrating these aspects in a future telecommunication system, such as a 6 G network. We observe that the current state-of-the-art primarily targets modellevel safety and explainability, but argue that there are bigger challenges at the system-level and runtime. As we move towards 6 G networks, there is an urgent need for new research directions to address these challenges. We aim to raise awareness among the research community about these critical issues.

Ericsson (Sweden) (SE)
Openalex Percentile: Top 91%
Adversarial Robustness in Machine Learning
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Safe and Explainable AI in 6G Telecommunication Systems: The Way Forward — Alexandros Palaios, Rafia Inam, et al. · (2025) | TGRS Research Map | TGRS