mlsanityData to 7 Oct 2026

Explainable Artificial Intelligence Approaches: A Survey

arXiv 2101.09429 ↗First posted 23 Jan 2021cs.AI cs.LGcited by 40

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Abstract (arXiv)

The lack of explainability of a decision from an Artificial Intelligence (AI) based "black box" system/model, despite its superiority in many real-world applications, is a key stumbling block for adopting AI in many high stakes applications of different domain or industry. While many popular Explainable Artificial Intelligence (XAI) methods or approaches are available to facilitate a human-friendly explanation of the decision, each has its own merits and demerits, with a plethora of open challenges. We demonstrate popular XAI methods with a mutual case study/task (i.e., credit default prediction), analyze for competitive advantages from multiple perspectives (e.g., local, global), provide meaningful insight on quantifying explainability, and recommend paths towards responsible or human-centered AI using XAI as a medium. Practitioners can use this work as a catalog to understand, compare, and correlate competitive advantages of popular XAI methods. In addition, this survey elicits future research directions towards responsible or human-centric AI systems, which is crucial to adopt AI in high stakes applications.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Builds on 29 of its 61 references that are in the map. First posted on arXiv 23 Jan 2021.

Builds on

29 of 61 references.

References · 29

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  2. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  3. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  4. 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,309
  5. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  6. 2016The Mythos of Model Interpretabilitycited by 716
  7. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  8. 2019A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAIcited by 403
  9. 2018Manipulating and Measuring Model Interpretabilitycited by 256
  10. 2018Learning to Explain: An Information-Theoretic Perspective on Model Interpretationcited by 248
  11. 2017Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Modelscited by 202
  12. 2018Visual Interpretability for Deep Learning: a Surveycited by 155
17 more
  1. 2020Causal Interpretability for Machine Learning -- Problems, Methods and Evaluationcited by 82
  2. 2016Scalable Bayesian Rule Listscited by 64
  3. 2019Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AIcited by 63
  4. 2018Explanation Methods in Deep Learning: Users, Values, Concerns and Challengescited by 49
  5. 2019Assessing the Local Interpretability of Machine Learning Modelscited by 23
  6. 2018An Adversarial Approach for Explainable AI in Intrusion Detection Systemscited by 22
  7. 2019Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretabilitycited by 22
  8. 2019Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learningcited by 19
  9. 2018Explanations of model predictions with live and breakDown packagescited by 17
  10. 2016An unexpected unity among methods for interpreting model predictionscited by 16
  11. 2019Towards Quantification of Explainability in Explainable Artificial Intelligence Methodscited by 15
  12. 2018Instance-Level Explanations for Fraud Detection: A Case Studycited by 10
  13. 2019Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Responsecited by 10
  14. 2019Explainability and Adversarial Robustness for RNNscited by 7
  15. 2019Infusing domain knowledge in AI-based "black box" models for better explainability with application in bankruptcy predictioncited by 6
  16. 2020An Information-Theoretic Approach to Personalized Explainable Machine Learningcited by 3
  17. 2017TIP: Typifying the Interpretability of Procedurescited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 10; 2023: 10; 2024: 9; 2025: 6; 2026: 2.

40 papers.

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  1. 2021Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunitiescited by 125
  2. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 90
  3. 2022Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunitiescited by 25
  4. 2023Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorationscited by 18
  5. 2022Diagnosing AI Explanation Methods with Folk Concepts of Behaviorcited by 15
  6. 2024Exploring the Robustness of In-Context Learning with Noisy Labelscited by 13
  7. 2022medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Spacecited by 10
  8. 2024A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Futurecited by 9
  9. 2022A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directionscited by 8
  10. 2022Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliancecited by 7
  11. 2022On the Robustness of Explanations of Deep Neural Network Models: A Surveycited by 5
  12. 2024Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challengescited by 5
28 more
  1. 2025Trustworthy AI Must Account for Interactionscited by 4
  2. 2023CGXplain: Rule-Based Deep Neural Network Explanations Using Dual Linear Programscited by 3
  3. 2024Distribution Guided Active Feature Acquisitioncited by 3
  4. 2025Towards Transparent AI: A Survey on Explainable Language Modelscited by 3
  5. 2022Automatic Infectious Disease Classification Analysis with Concept Discoverycited by 2
  6. 2022Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directionscited by 2
  7. 2023The Contribution of Knowledge in Visiolinguistic Learning: A Survey on Tasks and Challengescited by 2
  8. 2023On the stability, correctness and plausibility of visual explanation methods based on feature importancecited by 2
  9. 2024Hybrid Convolutional Neural Networks with Reliability Guaranteecited by 2
  10. 2021Towards Explainable Artificial Intelligence in Banking and Financial Servicescited by 1
  11. 2022Explainability for identification of vulnerable groups in machine learning modelscited by 1
  12. 2023Multi-Layer Attention-Based Explainability via Transformers for Tabular Datacited by 1
  13. 2023EnTri: Ensemble Learning with Tri-level Representations for Explainable Scene Recognitioncited by 1
  14. 2023Utilizing Explainability Techniques for Reinforcement Learning Model Assurancecited by 1
  15. 2024Review of Explainable Graph-Based Recommender Systemscited by 1
  16. 2024Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detectioncited by 1
  17. 2025Performance Evaluation of Sentiment Analysis on Text and Emoji Data Using End-to-End, Transfer Learning, Distributed and Explainable AI Modelscited by 1
  18. 2022Reconnoitering the class distinguishing abilities of the features, to know them bettercited by 0
  19. 2023Data-efficient, Explainable and Safe Box Manipulation: Illustrating the Advantages of Physical Priors in Model-Predictive Controlcited by 0
  20. 2023Detection of Sensor-To-Sensor Variations using Explainable AIcited by 0
  21. 2023Teach Me How to Improve My Argumentation Skills: A Survey on Feedback in Argumentationcited by 0
  22. 2024No Dtrain: Model-Agnostic Counterfactual Explanations Using Reinforcement Learningcited by 0
  23. 2024A Scale-Invariant Diagnostic Approach Towards Understanding Dynamics of Deep Neural Networkscited by 0
  24. 2025Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Modelscited by 0
  25. 2025No Masks Needed: Explainable AI for Deriving Segmentation from Classificationcited by 0
  26. 2025Towards a Transparent and Interpretable AI Model for Medical Image Classificationscited by 0
  27. 2026Assessing Alignment and Stability of Feature Importance Explanations via Weight of Evidencecited by 0
  28. 2026Self-Verifying Anomaly Detection using Explainable AI for Cybersecurity of DER Networkscited by 0