mlsanityData to 14 Oct 2026

What Does Explainable AI Really Mean? A New Conceptualization of Perspectives

arXiv 1710.00794 ↗First posted 2 Oct 2017cs.AIcited by 72

Not on the map. Why?

Abstract (arXiv)

We characterize three notions of explainable AI that cut across research fields: opaque systems that offer no insight into its algo- rithmic mechanisms; interpretable systems where users can mathemat- ically analyze its algorithmic mechanisms; and comprehensible systems that emit symbols enabling user-driven explanations of how a conclusion is reached. The paper is motivated by a corpus analysis of NIPS, ACL, COGSCI, and ICCV/ECCV paper titles showing differences in how work on explainable AI is positioned in various fields. We close by introducing a fourth notion: truly explainable systems, where automated reasoning is central to output crafted explanations without requiring human post processing as final step of the generative process.

Key facts. Cited by 72 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Oct 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 22; 2022: 11; 2023: 20; 2024: 13; 2025: 3; 2026: 3.

72 papers.

Sort:

By area:

  1. 2021What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Researchcited by 119
  2. 2021Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Surveycited by 72
  3. 2022On scientific understanding with artificial intelligencecited by 70
  4. 2023Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulationcited by 61
  5. 2021Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applicationscited by 59
  6. 2021OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wildcited by 50
  7. 2021Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networkscited by 48
  8. 2024Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Directioncited by 46
  9. 2021To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methodscited by 26
  10. 2023`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Valuescited by 18
  11. 2021Explainable Machine Learning with Prior Knowledge: An Overviewcited by 15
  12. 2025Interpretable Machine Learning in Physics: A Reviewcited by 14
38 more
  1. 2021DiCE4EL: Interpreting Process Predictions using a Milestone-Aware Counterfactual Approachcited by 13
  2. 2022Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classificationcited by 12
  3. 2022Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applicationscited by 11
  4. 2023Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressionscited by 10
  5. 2021Development and testing of an image transformer for explainable autonomous driving systemscited by 9
  6. 2022XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learningcited by 8
  7. 2024Comprehensible Artificial Intelligence on Knowledge Graphs: A surveycited by 8
  8. 2024Evaluating Cultural Awareness of LLMs for Yoruba, Malayalam, and Englishcited by 8
  9. 2021Explainable AI and Adoption of Financial Algorithmic Advisors: an Experimental Studycited by 7
  10. 2021AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validationcited by 7
  11. 2024Neuro-Symbolic AI for Military Applicationscited by 7
  12. 2021Explainable Artificial Intelligence (XAI): An Engineering Perspectivecited by 6
  13. 2022Trade-off Between Efficiency and Consistency for Removal-based Explanationscited by 6
  14. 2024On the Value of Labeled Data and Symbolic Methods for Hidden Neuron Activation Analysiscited by 6
  15. 2023Users are the North Star for AI Transparencycited by 5
  16. 2023Explainable AI applications in the Medical Domain: a systematic reviewcited by 5
  17. 2023Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into Quantum Experimentscited by 5
  18. 2024A Comparative Analysis of Counterfactual Explanation Methods for Text Classifierscited by 5
  19. 2021Trustworthy AI and Robotics and the Implications for the AEC Industry: A Systematic Literature Review and Future Potentialscited by 4
  20. 2022Visual Ground Truth Construction as Faceted Classificationcited by 4
  21. 2021Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systemscited by 3
  22. 2021Quantifying the Task-Specific Information in Text-Based Classificationscited by 3
  23. 2021A Practical guide on Explainable AI Techniques applied on Biomedical use case applicationscited by 3
  24. 2023PACE: A Program Analysis Framework for Continuous Performance Predictioncited by 3
  25. 2024A Mechanistic Explanatory Strategy for XAIcited by 3
  26. 2023Semantic match: Debugging feature attribution methods in XAI for healthcarecited by 2
  27. 2023Explaining Deep Learning Hidden Neuron Activations using Concept Inductioncited by 2
  28. 2023Generalization-baed similaritycited by 2
  29. 2023Assisting clinical practice with fuzzy probabilistic decision treescited by 2
  30. 2023Fixing confirmation bias in feature attribution methods via semantic matchcited by 2
  31. 2023Understanding CNN Hidden Neuron Activations Using Structured Background Knowledge and Deductive Reasoningcited by 2
  32. 2023On the Multiple Roles of Ontologies in Explainable AIcited by 2
  33. 2024Leveraging Large Language Models for Hybrid Workplace Decision Supportcited by 2
  34. 2024Aligning Generalisation Between Humans and Machinescited by 2
  35. 2021Explaining Ridesharing: Selection of Explanations for Increasing User Satisfactioncited by 1
  36. 2021mSHAP: SHAP Values for Two-Part Modelscited by 1
  37. 2021Semantic Reasoning from Model-Agnostic Explanationscited by 1
  38. 2021Measuring Ethics in AI with AI: A Methodology and Dataset Constructioncited by 1

50 most cited of 72 All 72 (JSON)