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Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI

arXiv 1902.01876 ↗First posted 5 Feb 2019cs.AIcited by 63

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Abstract (arXiv)

This is an integrative review that address the question, "What makes for a good explanation?" with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain articles stand out by virtue of their particular relevance to XAI, and their methods, results, and key points are highlighted. It is recommended that AI/XAI researchers be encouraged to include in their research reports fuller details on their empirical or experimental methods, in the fashion of experimental psychology research reports: details on Participants, Instructions, Procedures, Tasks, Dependent Variables (operational definitions of the measures and metrics), Independent Variables (conditions), and Control Conditions.

Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Feb 2019.

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Citing papers per year: 2021: 19; 2022: 7; 2023: 16; 2024: 12; 2025: 7; 2026: 2.

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  1. 2021Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunitiescited by 125
  2. 2021What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Researchcited by 119
  3. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 90
  4. 2021If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniquescited by 82
  5. 2023Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision supportcited by 65
  6. 2021Explainable Artificial Intelligence Approaches: A Surveycited by 40
  7. 2022Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AIcited by 40
  8. 2021Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AIcited by 38
  9. 2021Principles of Explanation in Human-AI Systemscited by 25
  10. 2021Levels of explainable artificial intelligence for human-aligned conversational explanationscited by 21
  11. 2023Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Makingcited by 18
  12. 2025A Comprehensive Survey on Self-Interpretable Neural Networkscited by 18
38 more
  1. 2021Contrastive Explanations of Plans Through Model Restrictionscited by 16
  2. 2021A Survey on AI Assurancecited by 16
  3. 2023Calibrated Explanations: with Uncertainty Information and Counterfactualscited by 16
  4. 2022ECLAD: Extracting Concepts with Local Aggregated Descriptorscited by 15
  5. 2025Artificial Intelligence Should Genuinely Support Clinical Reasoning and Decision Making To Bridge the Translational Gapcited by 13
  6. 2022Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differcited by 12
  7. 2023A Comprehensive Review on Financial Explainable AIcited by 10
  8. 2021Efficient Explanations for Knowledge Compilation Languagescited by 9
  9. 2022A Meta Survey of Quality Evaluation Criteria in Explanation Methodscited by 8
  10. 2024Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessmentscited by 8
  11. 2021Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutionscited by 7
  12. 2022A Theoretical Framework for AI Models Explainability with Application in Biomedicinecited by 7
  13. 2023Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communicationcited by 7
  14. 2024Interactive Prompt Debugging with Sequence Saliencecited by 6
  15. 2024Why do explanations fail? A typology and discussion on failures in XAIcited by 6
  16. 2021Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authoritiescited by 5
  17. 2021Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challengescited by 5
  18. 2023Explainable AI And Visual Reasoning: Insights From Radiologycited by 5
  19. 2023A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI)cited by 5
  20. 2023Calibrated Explanations for Regressioncited by 5
  21. 2021Making Things Explainable vs Explaining: Requirements and Challenges under the GDPRcited by 4
  22. 2023Explainable Predictive Maintenancecited by 3
  23. 2024Information That Matters: Exploring Information Needs of People Affected by Algorithmic Decisionscited by 3
  24. 2024Cultural Bias in Explainable AI Research: A Systematic Analysiscited by 3
  25. 2022Explanation as Question Answering based on a Task Model of the Agent's Designcited by 2
  26. 2023Explaining Deep Learning Hidden Neuron Activations using Concept Inductioncited by 2
  27. 2023Automatic Textual Explanations of Concept Latticescited by 2
  28. 2024Leveraging Large Language Models for Hybrid Workplace Decision Supportcited by 2
  29. 2021Using Psychological Characteristics of Situations for Social Situation Comprehension in Support Agentscited by 1
  30. 2021Explanation as Question Answering based on Design Knowledgecited by 1
  31. 2024XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensicscited by 1
  32. 2024AI with Alien Content and Alien Metasemanticscited by 1
  33. 2024Surrogate Modeling for Explainable Predictive Time Series Correctionscited by 1
  34. 2025Holistic Explainable AI (H-XAI): Extending Transparency Beyond Developers in AI-Driven Decision Makingcited by 1
  35. 2021A Comparative Approach to Explainable Artificial Intelligence Methods in Application to High-Dimensional Electronic Health Records: Examining the Usability of XAIcited by 0
  36. 2021Towards an Explanation Space to Align Humans and Explainable-AI Teamworkcited by 0
  37. 2022Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spacescited by 0
  38. 2023When a CBR in Hand is Better than Twins in the Bushcited by 0

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