mlsanityData to 7 Oct 2026

A Survey on the Explainability of Supervised Machine Learning

arXiv 2011.07876 ↗First posted 16 Nov 2020cs.LG cs.AI stat.MLcited by 225

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Abstract (arXiv)

Predictions obtained by, e.g., artificial neural networks have a high accuracy but humans often perceive the models as black boxes. Insights about the decision making are mostly opaque for humans. Particularly understanding the decision making in highly sensitive areas such as healthcare or fifinance, is of paramount importance. The decision-making behind the black boxes requires it to be more transparent, accountable, and understandable for humans. This survey paper provides essential definitions, an overview of the different principles and methodologies of explainable Supervised Machine Learning (SML). We conduct a state-of-the-art survey that reviews past and recent explainable SML approaches and classifies them according to the introduced definitions. Finally, we illustrate principles by means of an explanatory case study and discuss important future directions.

Key facts. Cited by 225 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Nov 2020.

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Citing papers per year: 2021: 20; 2022: 43; 2023: 65; 2024: 48; 2025: 32; 2026: 17.

225 papers.

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  1. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 299
  2. 2024Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modificationscited by 220
  3. 2022Training Data Influence Analysis and Estimation: A Surveycited by 134
  4. 2021Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processingcited by 126
  5. 2021Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunitiescited by 125
  6. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  7. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 90
  8. 2022Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanationscited by 84
  9. 2022On scientific understanding with artificial intelligencecited by 68
  10. 2021GraphSVX: Shapley Value Explanations for Graph Neural Networkscited by 63
  11. 2024Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Reviewcited by 52
  12. 2022The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanationscited by 41
38 more
  1. 2022A Survey on Explainable Anomaly Detectioncited by 39
  2. 2022Going Beyond XAI: A Systematic Survey for Explanation-Guided Learningcited by 35
  3. 2021Open-world Machine Learning: Applications, Challenges, and Opportunitiescited by 34
  4. 2023Analyzing Feed-Forward Blocks in Transformers through the Lens of Attention Mapscited by 33
  5. 2024Kolmogorov-Arnold Graph Neural Networkscited by 31
  6. 2023AtMan: Understanding Transformer Predictions Through Memory Efficient Attention Manipulationcited by 30
  7. 2023A Survey on Explainability of Graph Neural Networkscited by 28
  8. 2023Using Sequences of Life-events to Predict Human Livescited by 27
  9. 2023Human-AI Coevolutioncited by 27
  10. 2021Choice modelling in the age of machine learning -- discussion papercited by 26
  11. 2022Human Interpretation of Saliency-based Explanation Over Textcited by 25
  12. 2023An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learningcited by 25
  13. 2022Slimmable Quantum Federated Learningcited by 23
  14. 2024XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Modelscited by 23
  15. 2022WeLM: A Well-Read Pre-trained Language Model for Chinesecited by 19
  16. 2021Surrogate Model-Based Explainability Methods for Point Cloud NNscited by 18
  17. 2025A Comprehensive Survey on Self-Interpretable Neural Networkscited by 18
  18. 2021Shallow decision trees for explainable k-means clusteringcited by 15
  19. 2022Diagnosing AI Explanation Methods with Folk Concepts of Behaviorcited by 15
  20. 2022ECLAD: Extracting Concepts with Local Aggregated Descriptorscited by 15
  21. 2023A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysiscited by 15
  22. 2024From Transparency to Accountability and Back: A Discussion of Access and Evidence in AI Auditingcited by 15
  23. 2021Explainable Machine Learning with Prior Knowledge: An Overviewcited by 14
  24. 2023Stability Guarantees for Feature Attributions with Multiplicative Smoothingcited by 14
  25. 2023Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UKcited by 13
  26. 2022Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunitiescited by 12
  27. 2021Efficient Decompositional Rule Extraction for Deep Neural Networkscited by 11
  28. 2022Explainability in reinforcement learning: perspective and positioncited by 11
  29. 2023Achieving Diversity in Counterfactual Explanations: a Review and Discussioncited by 11
  30. 2023Bridging the Transparency Gap: What Can Explainable AI Learn From the AI Act?cited by 10
  31. 2023Causal Explanations for Sequential Decision-Making in Multi-Agent Systemscited by 10
  32. 2024Efficient Exploration of the Rashomon Set of Rule Set Modelscited by 10
  33. 2022Is explainable AI a race against model complexity?cited by 9
  34. 2024Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspectivecited by 9
  35. 2024A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Futurecited by 9
  36. 2022XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learningcited by 8
  37. 2023Fairness Aware Counterfactuals for Subgroupscited by 8
  38. 2024Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for Transformerscited by 8

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