mlsanityData to 7 Oct 2026

What will it take to generate fairness-preserving explanations?

arXiv 2106.13346 ↗First posted 24 Jun 2021cs.LG cs.AI cs.CYcited by 6

Not on the map. Why?

Abstract (arXiv)

In situations where explanations of black-box models may be useful, the fairness of the black-box is also often a relevant concern. However, the link between the fairness of the black-box model and the behavior of explanations for the black-box is unclear. We focus on explanations applied to tabular datasets, suggesting that explanations do not necessarily preserve the fairness properties of the black-box algorithm. In other words, explanation algorithms can ignore or obscure critical relevant properties, creating incorrect or misleading explanations. More broadly, we propose future research directions for evaluating and generating explanations such that they are informative and relevant from a fairness perspective.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 24 Jun 2021.

Builds on

15 of 22 references.

References · 15

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  4. 2019Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methodscited by 440
  5. 2019On the (In)fidelity and Sensitivity for Explanationscited by 309
  6. 2020Problems with Shapley-value-based explanations as feature importance measurescited by 198
  7. 2020Evaluating and Aggregating Feature-based Model Explanationscited by 185
  8. 2019On the Apparent Conflict Between Individual and Group Fairnesscited by 126
  9. 2020Reliable Post hoc Explanations: Modeling Uncertainty in Explainabilitycited by 125
  10. 2019"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanationscited by 111
  11. 2019Fairwashing: the risk of rationalizationcited by 81
  12. 2018iFair: Learning Individually Fair Data Representations for Algorithmic Decision Makingcited by 64
3 more
  1. 2021Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needscited by 64
  2. 2020Machine Learning Explainability for External Stakeholderscited by 17
  3. 2021Epistemic values in feature importance methods: Lessons from feminist epistemologycited by 11

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 1; 2024: 2.

6 papers.

  1. 2022The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanationscited by 41
  2. 2022What Makes a Good Explanation?: A Harmonized View of Properties of Explanationscited by 21
  3. 2024Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictionscited by 13
  4. 2024Understanding Disparities in Post Hoc Machine Learning Explanationcited by 6
  5. 2023SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learningcited by 4
  6. 2022Tensions Between the Proxies of Human Values in AIcited by 0