mlsanityData to 14 Oct 2026

Explainable Artificial Intelligence (XAI) for Internet of Things: A Survey

arXiv 2206.04800 ↗First posted 7 Jun 2022cs.AI cs.LG cs.NIcited by 8

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Abstract (arXiv)

Black-box nature of Artificial Intelligence (AI) models do not allow users to comprehend and sometimes trust the output created by such model. In AI applications, where not only the results but also the decision paths to the results are critical, such black-box AI models are not sufficient. Explainable Artificial Intelligence (XAI) addresses this problem and defines a set of AI models that are interpretable by the users. Recently, several number of XAI models have been to address the issues surrounding by lack of interpretability and explainability of black-box models in various application areas such as healthcare, military, energy, financial and industrial domains. Although the concept of XAI has gained great deal of attention recently, its integration into the IoT domain has not yet been fully defined. In this paper, we provide an in-depth and systematic review of recent studies using XAI models in the scope of IoT domain. We categorize the studies according to their methodology and applications areas. In addition, we aim to focus on the challenging problems and open issues and give future directions to guide the developers and researchers for prospective future investigations.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 36 of its 141 references that are in the map. First posted on arXiv 7 Jun 2022.

Builds on

36 of 141 references.

References · 36

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24 more
  1. 2017Interpretable Explanations of Black Boxes by Meaningful Perturbationcited by 612
  2. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  3. 2019A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAIcited by 404
  4. 2018Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negativescited by 262
  5. 2020Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Surveycited by 254
  6. 2016Understanding Neural Networks through Representation Erasurecited by 224
  7. 2016Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Modelscited by 213
  8. 2017Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Modelscited by 206
  9. 2017Visualizing Deep Neural Network Decisions: Prediction Difference Analysiscited by 166
  10. 2020Explainable Artificial Intelligence: a Systematic Reviewcited by 79
  11. 2021Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Surveycited by 72
  12. 2018Transparency and Explanation in Deep Reinforcement Learning Neural Networkscited by 40
  13. 2019When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signaturescited by 40
  14. 2019Explaining Models by Propagating Shapley Values of Local Componentscited by 29
  15. 2020Making Fair ML Software using Trustworthy Explanationcited by 24
  16. 2021An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinerycited by 22
  17. 2020Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoringcited by 20
  18. 2021Explainable Activity Recognition for Smart Home Systemscited by 14
  19. 2020Explainable AI meets Healthcare: A Study on Heart Disease Datasetcited by 10
  20. 2022TRUST XAI: Model-Agnostic Explanations for AI With a Case Study on IIoT Securitycited by 9
  21. 2021Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systemscited by 7
  22. 2022Explainable Anomaly Detection for Industrial Control System Cybersecuritycited by 5
  23. 2020An Experimentation Platform for Explainable Coalition Situational Understandingcited by 1
  24. 2021Zero-bias Deep Learning Enabled Quick and Reliable Abnormality Detection in IoTcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 3; 2025: 3; 2026: 1.

8 papers.

  1. 2022Explainable AI for clinical and remote health applications: a survey on tabular and time series datacited by 24
  2. 2024When IoT Meet LLMs: Applications and Challengescited by 11
  3. 2025Which LIME should I trust? Concepts, Challenges, and Solutionscited by 8
  4. 2024AgroXAI: Explainable AI-Driven Crop Recommendation System for Agriculture 4.0cited by 2
  5. 2025A Survey on Interpretability in Visual Recognitioncited by 2
  6. 2025Towards Explainable Indoor Localization: Interpreting Neural Network Learning on Wi-Fi Fingerprints Using Logic Gatescited by 1
  7. 2024A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networkscited by 0
  8. 2026Agentic IoT: Architectures, Applications, and Challenges Toward the Internet of Agentscited by 0