mlsanityData to 7 Oct 2026

Reviewing the Need for Explainable Artificial Intelligence (xAI)

arXiv 2012.01007 ↗First posted 2 Dec 2020cs.HCcited by 29

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Abstract (arXiv)

The diffusion of artificial intelligence (AI) applications in organizations and society has fueled research on explaining AI decisions. The explainable AI (xAI) field is rapidly expanding with numerous ways of extracting information and visualizing the output of AI technologies (e.g. deep neural networks). Yet, we have a limited understanding of how xAI research addresses the need for explainable AI. We conduct a systematic review of xAI literature on the topic and identify four thematic debates central to how xAI addresses the black-box problem. Based on this critical analysis of the xAI scholarship we synthesize the findings into a future research agenda to further the xAI body of knowledge.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Dec 2020.

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Citing papers per year: 2021: 2; 2022: 10; 2023: 6; 2024: 8; 2025: 2; 2026: 1.

29 papers.

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  1. 2022Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future researchcited by 20
  2. 2022Explainability in reinforcement learning: perspective and positioncited by 11
  3. 2022XAI for Cybersecurity: State of the Art, Challenges, Open Issues and Future Directionscited by 10
  4. 2024A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Futurecited by 9
  5. 2023On the Impact of Knowledge Distillation for Model Interpretabilitycited by 8
  6. 2022Quality Diversity Evolutionary Learning of Decision Treescited by 7
  7. 2021Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authoritiescited by 5
  8. 2022Improving Interpretability via Regularization of Neural Activation Sensitivitycited by 5
  9. 2022Painting the black box white: experimental findings from applying XAI to an ECG reading settingcited by 4
  10. 2024Explaining Decisions of Agents in Mixed-Motive Gamescited by 4
  11. 2022On the Relationship Between Explanations, Fairness Perceptions, and Decisionscited by 3
  12. 2024Cultural Bias in Explainable AI Research: A Systematic Analysiscited by 3
17 more
  1. 2022Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directionscited by 2
  2. 2023Beyond Single-Feature Importance with ICECREAMcited by 2
  3. 2022Machine Learning in Transaction Monitoring: The Prospect of xAIcited by 1
  4. 2023Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposalcited by 1
  5. 2023Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Workcited by 1
  6. 2023QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systemscited by 1
  7. 2024Graph Neural Network and NER-Based Text Summarizationcited by 1
  8. 2024Why Reinforcement Learning in Energy Systems Needs Explanationscited by 1
  9. 2025Measuring the (Un)Faithfulness of Concept-Based Explanationscited by 1
  10. 2021Abstraction, Validation, and Generalization for Explainable Artificial Intelligencecited by 0
  11. 2022Explainable Artificial Intelligence in Construction: The Content, Context, Process, Outcome Evaluation Frameworkcited by 0
  12. 2023Notion of Explainable Artificial Intelligence -- An Empirical Investigation from A Users Perspectivecited by 0
  13. 2024Fuzzy Logic Function as a Post-hoc Explanator of the Nonlinear Classifiercited by 0
  14. 2024Influence based explainability of brain tumors segmentation in multimodal Magnetic Resonance Imagingcited by 0
  15. 2024The Literature Review Network: An Explainable Artificial Intelligence for Systematic Literature Reviews, Meta-analyses, and Method Developmentcited by 0
  16. 2025A Framework for Causal Concept-based Model Explanationscited by 0
  17. 2026Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildingscited by 0