mlsanityData to 7 Oct 2026

A Critical Survey on Fairness Benefits of Explainable AI

arXiv 2310.13007 ↗First posted 15 Oct 2023cs.AIcited by 16

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Abstract (arXiv)

In this critical survey, we analyze typical claims on the relationship between explainable AI (XAI) and fairness to disentangle the multidimensional relationship between these two concepts. Based on a systematic literature review and a subsequent qualitative content analysis, we identify seven archetypal claims from 175 scientific articles on the alleged fairness benefits of XAI. We present crucial caveats with respect to these claims and provide an entry point for future discussions around the potentials and limitations of XAI for specific fairness desiderata. Importantly, we notice that claims are often (i) vague and simplistic, (ii) lacking normative grounding, or (iii) poorly aligned with the actual capabilities of XAI. We suggest to conceive XAI not as an ethical panacea but as one of many tools to approach the multidimensional, sociotechnical challenge of algorithmic fairness. Moreover, when making a claim about XAI and fairness, we emphasize the need to be more specific about what kind of XAI method is used, which fairness desideratum it refers to, how exactly it enables fairness, and who is the stakeholder that benefits from XAI.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 94 of its 235 references that are in the map. First posted on arXiv 15 Oct 2023.

Builds on

94 of 235 references.

References · 94

  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. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  5. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  6. 2016The Mythos of Model Interpretabilitycited by 716
  7. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
  8. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  9. 2019Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methodscited by 440
  10. 2020Fairness in Machine Learning: A Surveycited by 369
  11. 2016European Union regulations on algorithmic decision-making and a "right to explanation"cited by 343
  12. 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
38 more
  1. 2018A comparative study of fairness-enhancing interventions in machine learningcited by 208
  2. 2018Path-Specific Counterfactual Fairnesscited by 179
  3. 2019Predictive Multiplicity in Classificationcited by 162
  4. 2017Fairness in Machine Learning: Lessons from Political Philosophycited by 148
  5. 2020Algorithmic Fairnesscited by 143
  6. 2020Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertaintycited by 128
  7. 2019On the Apparent Conflict Between Individual and Group Fairnesscited by 126
  8. 2019Fairness in Deep Learning: A Computational Perspectivecited by 123
  9. 2021What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Researchcited by 119
  10. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  11. 2021Counterfactual Explanations Can Be Manipulatedcited by 113
  12. 2019"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanationscited by 111
  13. 2019Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approachescited by 101
  14. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  15. 2018'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisionscited by 91
  16. 2019Fairwashing: the risk of rationalizationcited by 81
  17. 2023Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanationscited by 80
  18. 2019Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Makingcited by 75
  19. 2019Shapley Q-value: A Local Reward Approach to Solve Global Reward Gamescited by 71
  20. 2020Fairwashing Explanations with Off-Manifold Detergentcited by 71
  21. 2019Understanding artificial intelligence ethics and safetycited by 68
  22. 2019FairSight: Visual Analytics for Fairness in Decision Makingcited by 59
  23. 2019Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learningcited by 57
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  26. 2021The Use and Misuse of Counterfactuals in Ethical Machine Learningcited by 52
  27. 2019FlipTest: Fairness Testing via Optimal Transportcited by 51
  28. 2018Explanation Methods in Deep Learning: Users, Values, Concerns and Challengescited by 49
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  30. 2021NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identificationcited by 42
  31. 2020Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differencescited by 41
  32. 2022Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanationscited by 40
  33. 2020What's Sex Got To Do With Fair Machine Learning?cited by 37
  34. 2019Explainability in Human-Agent Systemscited by 34
  35. 2018TED: Teaching AI to Explain its Decisionscited by 33
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  37. 2019Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgmentcited by 31
  38. 2020Algorithmic Fairness from a Non-ideal Perspectivecited by 30

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 8; 2026: 5.

16 papers.

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  1. 2024Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairnesscited by 5
  2. 2025SHAP-based Explanations are Sensitive to Feature Representationcited by 4
  3. 2025Beware of "Explanations" of AIcited by 3
  4. 2024Rolling in the deep of cognitive and AI biasescited by 2
  5. 2025Explanations as Bias Detectors: A Critical Study of Local Post-hoc XAI Methods for Fairness Explorationcited by 2
  6. 2025Mind the Gap! Pathways Towards Unifying AI Safety and Ethics Researchcited by 2
  7. 2026From Untamed Black Box to Interpretable Pedagogical Orchestration: The Ensemble of Specialized LLMs Architecture for Adaptive Tutoringcited by 2
  8. 2023Wrapper Boxes: Faithful Attribution of Model Predictions to Training Datacited by 1
  9. 2025Towards Meaningful Transparency in Civic AI Systemscited by 1
  10. 2026Beyond Abstract Compliance: Operationalising trust in AI as a moral relationshipcited by 1
  11. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  12. 2025Bridging Fairness and Explainability: Can Input-Based Explanations Promote Fairness in Hate Speech Detection?cited by 0
4 more
  1. 2025Strategic Innovation Management in the Age of Large Language Models Market Intelligence, Adaptive R&D, and Ethical Governancecited by 0
  2. 2026Procedural Fairness via Group Counterfactual Explanationcited by 0
  3. 2026Where are the Humans? A Scoping Review of Fairness in Multi-agent AI Systemscited by 0
  4. 2026We Need Explanation Cards to Connect Explanation Algorithms to the Real Worldcited by 0