mlsanityData to 7 Oct 2026

Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI

arXiv 2106.07483 ↗First posted 14 Jun 2021cs.CY cs.AI cs.LGcited by 16

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Abstract (arXiv)

Many ML models are opaque to humans, producing decisions too complex for humans to easily understand. In response, explainable artificial intelligence (XAI) tools that analyze the inner workings of a model have been created. Despite these tools' strength in translating model behavior, critiques have raised concerns about the impact of XAI tools as a tool for `fairwashing` by misleading users into trusting biased or incorrect models. In this paper, we created a framework for evaluating explainable AI tools with respect to their capabilities for detecting and addressing issues of bias and fairness as well as their capacity to communicate these results to their users clearly. We found that despite their capabilities in simplifying and explaining model behavior, many prominent XAI tools lack features that could be critical in detecting bias. Developers can use our framework to suggest modifications needed in their toolkits to reduce issues likes fairwashing.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 14 of its 35 references that are in the map. First posted on arXiv 14 Jun 2021.

Builds on

14 of 35 references.

References · 14

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  2. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  3. 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
  4. 2017Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Modelscited by 202
  5. 2019One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniquescited by 164
  6. 2018The Frontiers of Fairness in Machine Learningcited by 139
  7. 2019On the Apparent Conflict Between Individual and Group Fairnesscited by 126
  8. 2019Fairwashing: the risk of rationalizationcited by 81
  9. 2019The relationship between trust in AI and trustworthy machine learning technologiescited by 78
  10. 2019Predictive Inequity in Object Detectioncited by 77
  11. 2017On Formalizing Fairness in Prediction with Machine Learningcited by 65
  12. 2018Explanation Methods in Deep Learning: Users, Values, Concerns and Challengescited by 49
2 more
  1. 2018Racial categories in machine learningcited by 48
  2. 2019Proposed Guidelines for the Responsible Use of Explainable Machine Learningcited by 8

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Citing papers per year: 2022: 4; 2023: 7; 2024: 2; 2025: 3.

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  1. 2022Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanationscited by 40
  2. 2022Challenges in Applying Explainability Methods to Improve the Fairness of NLP Modelscited by 20
  3. 2023Characterizing Large Language Models as Rationalizers of Knowledge-intensive Taskscited by 9
  4. 2024Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspectivecited by 9
  5. 2022Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Boxcited by 7
  6. 2023Disentangling and Operationalizing AI Fairness at LinkedIncited by 7
  7. 2022Explainable Global Fairness Verification of Tree-Based Classifierscited by 5
  8. 2025On the Design and Evaluation of Human-centered Explainable AI Systems: A Systematic Review and Taxonomycited by 2
  9. 2023Towards a Praxis for Intercultural Ethics in Explainable AIcited by 1
  10. 2023Explainable AI in Orthopedics: Challenges, Opportunities, and Prospectscited by 1
  11. 2024Bias Neutralization Framework: Measuring Fairness in Large Language Models with Bias Intelligence Quotient (BiQ)cited by 1
  12. 2025On Identifying Why and When Foundation Models Perform Well on Time-Series Forecasting Using Automated Explanations and Ratingcited by 1
4 more
  1. 2025An Analysis of the New EU AI Act and A Proposed Standardization Framework for Machine Learning Fairnesscited by 1
  2. 2023Fairness Explainability using Optimal Transport with Applications in Image Classificationcited by 0
  3. 2023Beyond XAI:Obstacles Towards Responsible AIcited by 0
  4. 2023Is Machine Learning Unsafe and Irresponsible in Social Sciences? Paradoxes and Reconsidering from Recidivism Prediction Taskscited by 0