mlsanityData to 7 Oct 2026

What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research

arXiv 2102.07817 ↗First posted 15 Feb 2021cs.AI cs.HCcited by 119

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Abstract (arXiv)

Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these stakeholders' desiderata) in a variety of contexts. However, the literature on XAI is vast, spreads out across multiple largely disconnected disciplines, and it often remains unclear how explainability approaches are supposed to achieve the goal of satisfying stakeholders' desiderata. This paper discusses the main classes of stakeholders calling for explainability of artificial systems and reviews their desiderata. We provide a model that explicitly spells out the main concepts and relations necessary to consider and investigate when evaluating, adjusting, choosing, and developing explainability approaches that aim to satisfy stakeholders' desiderata. This model can serve researchers from the variety of different disciplines involved in XAI as a common ground. It emphasizes where there is interdisciplinary potential in the evaluation and the development of explainability approaches.

Key facts. Cited by 119 papers in the map (data through 7 Oct 2026). Builds on 46 of its 213 references that are in the map. First posted on arXiv 15 Feb 2021.

Builds on

46 of 213 references.

References · 46

  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. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  5. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  6. 2018Model Cards for Model Reportingcited by 1,068
  7. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  8. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  9. 2016The Mythos of Model Interpretabilitycited by 716
  10. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  11. 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380
  12. 2016European Union regulations on algorithmic decision-making and a "right to explanation"cited by 343
34 more
  1. 2017Methods for Interpreting and Understanding Deep Neural Networkscited by 342
  2. 2018Metrics for Explainable AI: Challenges and Prospectscited by 257
  3. 2017Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Modelscited by 202
  4. 2019Explainable Reinforcement Learning Through a Causal Lenscited by 177
  5. 2019One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniquescited by 164
  6. 2018Explaining Explanations in AIcited by 136
  7. 2019Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approachescited by 101
  8. 2018'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisionscited by 91
  9. 2017Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciencescited by 84
  10. 2015The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classificationcited by 76
  11. 2017What Does Explainable AI Really Mean? A New Conceptualization of Perspectivescited by 72
  12. 2020One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparencycited by 64
  13. 2019Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AIcited by 63
  14. 2020The Pragmatic Turn in Explainable Artificial Intelligence (XAI)cited by 53
  15. 2018Explanation Methods in Deep Learning: Users, Values, Concerns and Challengescited by 49
  16. 2015The social dilemma of autonomous vehiclescited by 47
  17. 2020A systematic review and taxonomy of explanations in decision support and recommender systemscited by 45
  18. 2019How model accuracy and explanation fidelity influence user trustcited by 44
  19. 2020Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differencescited by 41
  20. 2017Explainable Planningcited by 40
  21. 2019Human-Centered Artificial Intelligence and Machine Learningcited by 37
  22. 2019Explainability in Human-Agent Systemscited by 34
  23. 2018TED: Teaching AI to Explain its Decisionscited by 33
  24. 2019Exploring Computational User Models for Agent Policy Summarizationcited by 29
  25. 2015Analyzing Classifiers: Fisher Vectors and Deep Neural Networkscited by 24
  26. 2019A Grounded Interaction Protocol for Explainable Artificial Intelligencecited by 22
  27. 2018Explainable Software Analyticscited by 15
  28. 2019Counterfactual States for Atari Agents via Generative Deep Learningcited by 14
  29. 2018Plan Explanations as Model Reconciliation -- An Empirical Studycited by 7
  30. 2019Explaining Explanations to Societycited by 6
  31. 2020Explanations of Black-Box Model Predictions by Contextual Importance and Utilitycited by 6
  32. 2019Abstraction for Zooming-In to Unsolvability Reasons of Grid-Cell Problemscited by 2
  33. 2019Local Score Dependent Model Explanation for Time Dependent Covariatescited by 1
  34. 2019A New Approach for Explainable Multiple Organ Annotation with Few Datacited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 15; 2023: 32; 2024: 23; 2025: 25; 2026: 13.

119 papers.

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  1. 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 123
  2. 2023AI Transparency in the Age of LLMs: A Human-Centered Research Roadmapcited by 119
  3. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 90
  4. 2022"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interactioncited by 63
  5. 2021NICE: An Algorithm for Nearest Instance Counterfactual Explanationscited by 62
  6. 2022Concept Activation Regions: A Generalized Framework For Concept-Based Explanationscited by 57
  7. 2022Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contextscited by 53
  8. 2024Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Reviewcited by 52
  9. 2024Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Directioncited by 46
  10. 2025Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checkingcited by 29
  11. 2025Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistantcited by 26
  12. 2022Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Makingcited by 25
38 more
  1. 2023PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Predictioncited by 25
  2. 2022"There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Makingcited by 19
  3. 2023Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorationscited by 18
  4. 2025A Comprehensive Survey on Self-Interpretable Neural Networkscited by 18
  5. 2023XAIR: A Framework of Explainable AI in Augmented Realitycited by 17
  6. 2023A Critical Survey on Fairness Benefits of Explainable AIcited by 16
  7. 2023Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Studycited by 15
  8. 2021Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligencecited by 13
  9. 2022The Conflict Between Explainable and Accountable Decision-Making Algorithmscited by 13
  10. 2023Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariancecited by 13
  11. 2024Understanding Inter-Concept Relationships in Concept-Based Modelscited by 13
  12. 2023Being Right for Whose Right Reasons?cited by 12
  13. 2024Path-based Explanation for Knowledge Graph Completioncited by 11
  14. 2023Bridging the Transparency Gap: What Can Explainable AI Learn From the AI Act?cited by 10
  15. 2023Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI)cited by 10
  16. 2024Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanationscited by 10
  17. 2021How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practicecited by 9
  18. 2021Appropriate Fairness Perceptions? On the Effectiveness of Explanations in Enabling People to Assess the Fairness of Automated Decision Systemscited by 9
  19. 2024Incremental XAI: Memorable Understanding of AI with Incremental Explanationscited by 8
  20. 2022Quality Diversity Evolutionary Learning of Decision Treescited by 7
  21. 2025Evaluating the Effectiveness of XAI Techniques for Encoder-Based Language Modelscited by 7
  22. 2025A Unified Framework with Novel Metrics for Evaluating the Effectiveness of XAI Techniques in LLMscited by 7
  23. 2023Generative Perturbation Analysis for Probabilistic Black-Box Anomaly Attributioncited by 6
  24. 2024What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blockscited by 6
  25. 2024Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Modelscited by 6
  26. 2021Yes We Care! -- Certification for Machine Learning Methods through the Care Label Frameworkcited by 5
  27. 2022A Means-End Account of Explainable Artificial Intelligencecited by 5
  28. 2023Blaming Humans and Machines: What Shapes People's Reactions to Algorithmic Harmcited by 5
  29. 2023Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Studycited by 5
  30. 2023A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI)cited by 5
  31. 2024Triamese-ViT: A 3D-Aware Method for Robust Brain Age Estimation from MRIscited by 5
  32. 2021Explanatory Pluralism in Explainable AIcited by 4
  33. 2021Explanation as a process: user-centric construction of multi-level and multi-modal explanationscited by 4
  34. 2022AI, Opacity, and Personal Autonomycited by 4
  35. 2022Explainable Artificial Intelligence: Precepts, Methods, and Opportunities for Research in Constructioncited by 4
  36. 2023Diagrammatization and Abduction to Improve AI Interpretability With Domain-Aligned Explanations for Medical Diagnosiscited by 4
  37. 2023Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Surveycited by 4
  38. 2025Are explainable AI (XAI) evaluation strategies aligned? Comparing subjective, objective, and mathematical evaluation measures using saliency mapscited by 4

50 most cited of 119 All 119 (JSON)