mlsanityData to 7 Oct 2026

Explainable Artificial Intelligence: a Systematic Review

arXiv 2006.00093 ↗First posted 29 May 2020cs.AI cs.LGcited by 79

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Abstract (arXiv)

Explainable Artificial Intelligence (XAI) has experienced a significant growth over the last few years. This is due to the widespread application of machine learning, particularly deep learning, that has led to the development of highly accurate models but lack explainability and interpretability. A plethora of methods to tackle this problem have been proposed, developed and tested. This systematic review contributes to the body of knowledge by clustering these methods with a hierarchical classification system with four main clusters: review articles, theories and notions, methods and their evaluation. It also summarises the state-of-the-art in XAI and recommends future research directions.

Key facts. Cited by 79 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 May 2020.

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Citing papers per year: 2021: 21; 2022: 19; 2023: 14; 2024: 17; 2025: 5; 2026: 3.

79 papers.

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  1. 2023Large Language Model Alignment: A Surveycited by 241
  2. 2021Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunitiescited by 125
  3. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 90
  4. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  5. 2021Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directionscited by 73
  6. 2024Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Reviewcited by 52
  7. 2022Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvementcited by 48
  8. 2021A Review on Explainability in Multimodal Deep Neural Netscited by 46
  9. 2024Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Directioncited by 46
  10. 2021What to Learn, and How: Toward Effective Learning from Rationalescited by 25
  11. 2022Concept Embedding Analysis: A Reviewcited by 20
  12. 2021A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Datacited by 19
38 more
  1. 2023Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterraneancited by 18
  2. 2022Lorentz group equivariant autoencoderscited by 15
  3. 2021XAI Handbook: Towards a Unified Framework for Explainable AIcited by 14
  4. 2021Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligencecited by 13
  5. 2024Explainable Artificial Intelligence for Medical Applications: A Reviewcited by 12
  6. 2022Privacy Meets Explainability: A Comprehensive Impact Benchmarkcited by 9
  7. 2022Explainable Artificial Intelligence (XAI) for Internet of Things: A Surveycited by 8
  8. 2021Explainable AI and Adoption of Financial Algorithmic Advisors: an Experimental Studycited by 7
  9. 2022Explainable AI for High Energy Physicscited by 7
  10. 2022Towards trustworthy Energy Disaggregation: A review of challenges, methods and perspectives for Non-Intrusive Load Monitoringcited by 7
  11. 2023Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature Spacescited by 7
  12. 2023Explainable Fraud Detection with Deep Symbolic Classificationcited by 7
  13. 2022A Detailed Study of Interpretability of Deep Neural Network based Top Taggerscited by 6
  14. 2024What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blockscited by 6
  15. 2024Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmapcited by 6
  16. 2021A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interactioncited by 5
  17. 2021Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challengescited by 5
  18. 2022Explainable Misinformation Detection Across Multiple Social Media Platformscited by 5
  19. 2024How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Surveycited by 5
  20. 2021Explanatory Pluralism in Explainable AIcited by 4
  21. 2022The Roles and Modes of Human Interactions with Automated Machine Learning Systemscited by 4
  22. 2022FAIR AI Models in High Energy Physicscited by 4
  23. 2023Robust Ante-hoc Graph Explainer using Bilevel Optimizationcited by 4
  24. 2023Identifying Drivers of Predictive Aleatoric Uncertaintycited by 4
  25. 2024Pantypes: Diverse Representatives for Self-Explainable Modelscited by 4
  26. 2024Enhancing Robot Explanation Capabilities through Vision-Language Models: a Preliminary Study by Interpreting Visual Inputs for Improved Human-Robot Interactioncited by 4
  27. 2023Interpretable Alzheimer's Disease Classification Via a Contrastive Diffusion Autoencodercited by 3
  28. 2025Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networkscited by 3
  29. 2021Toward Affective XAI: Facial Affect Analysis for Understanding Explainable Human-AI Interactionscited by 2
  30. 2021Instructive artificial intelligence (AI) for human training, assistance, and explainabilitycited by 2
  31. 2021Explaining Face Presentation Attack Detection Using Natural Languagecited by 2
  32. 2022Multi-Fault Diagnosis Of Industrial Rotating Machines Using Data-Driven Approach: A Review Of Two Decades Of Researchcited by 2
  33. 2022Responsibility: An Example-based Explainable AI approach via Training Process Inspectioncited by 2
  34. 2022Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directionscited by 2
  35. 2023A Voting Approach for Explainable Classification with Rule Learningcited by 2
  36. 2024XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniquescited by 2
  37. 2024Let the Fuzzy Rule Speak: Enhancing In-context Learning Debiasing with Interpretabilitycited by 2
  38. 2021How to Explain Neural Networks: an Approximation Perspectivecited by 1

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