mlsanityData to 7 Oct 2026

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

arXiv 2112.04698 ↗First posted 9 Dec 2021cs.NI cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency, and an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made in real-time. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed, data-intensive AI decision-making beyond designers' and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black-box AI decision-making process. This paper surveys the application of XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for 6G in the near future.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 68 of its 288 references that are in the map. First posted on arXiv 9 Dec 2021.

Builds on

68 of 288 references.

References · 68

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  3. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  4. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  5. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  6. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  7. 2020The Future of Digital Health with Federated Learningcited by 625
  8. 2019A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAIcited by 403
  9. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 364
  10. 2018Consistent Individualized Feature Attribution for Tree Ensemblescited by 279
  11. 2018Metrics for Explainable AI: Challenges and Prospectscited by 257
  12. 2020Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Surveycited by 252
38 more
  1. 2019Causality for Machine Learningcited by 242
  2. 2016Model-Agnostic Interpretability of Machine Learningcited by 219
  3. 2018Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learningcited by 203
  4. 2018Programmatically Interpretable Reinforcement Learningcited by 192
  5. 2019Explainable Reinforcement Learning Through a Causal Lenscited by 177
  6. 2019One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniquescited by 164
  7. 2017Interpretable Convolutional Neural Networkscited by 161
  8. 2019A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problemscited by 157
  9. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 150
  10. 2020Explainable deep learning models in medical image analysiscited by 111
  11. 20206G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communicationscited by 108
  12. 2019Towards Explainable Artificial Intelligencecited by 106
  13. 2020Explainability in Deep Reinforcement Learningcited by 96
  14. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 82
  15. 2018In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learningcited by 80
  16. 2020Explainable Artificial Intelligence: a Systematic Reviewcited by 79
  17. 2017Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learningcited by 77
  18. 2020Secure and Robust Machine Learning for Healthcare: A Surveycited by 77
  19. 2020Interpretability of machine learning based prediction models in healthcarecited by 75
  20. 2019Towards a Rigorous Evaluation of XAI Methods on Time Seriescited by 67
  21. 2020When Machine Learning Meets Privacy: A Survey and Outlookcited by 67
  22. 2019Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AIcited by 63
  23. 2019Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanationscited by 63
  24. 2018RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Recordscited by 61
  25. 2020Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosiscited by 47
  26. 2018TED: Teaching AI to Explain its Decisionscited by 33
  27. 2019Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Reviewcited by 31
  28. 2019Evaluating Recurrent Neural Network Explanationscited by 29
  29. 2020When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Networkcited by 29
  30. 2019Artificial Intelligence-Enabled Intelligent 6G Networkscited by 27
  31. 2019Artificial Intelligence-Enabled Cellular Networks: A Critical Path to Beyond-5G and 6Gcited by 25
  32. 2020Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Datasetcited by 22
  33. 2019Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challengescited by 18
  34. 2020xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systemscited by 18
  35. 2017A Berkeley View of Systems Challenges for AIcited by 15
  36. 2020Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Taskscited by 15
  37. 2018Explainable Securitycited by 14
  38. 2018Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligencecited by 14

50 most cited of 68 All 68 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 1.

5 papers.

  1. 2025A Statistical Case Against Empirical Human-AI Alignmentcited by 6
  2. 2023Towards Bridging the FL Performance-Explainability Trade-Off: A Trustworthy 6G RAN Slicing Use-Casecited by 2
  3. 2024A Survey on the Integration of Generative AI for Critical Thinking in Mobile Networkscited by 1
  4. 2026Generative Explainability for Next-Generation Networks: LLM-Augmented XAI with Mutual Feature Interactionscited by 1
  5. 2025Integration of TinyML and LargeML: A Survey of 6G and Beyondcited by 0