mlsanityData to 14 Oct 2026

Explainable Global Fairness Verification of Tree-Based Classifiers

arXiv 2209.13179 ↗First posted 27 Sep 2022cs.LG cs.LOcited by 5

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Abstract (arXiv)

We present a new approach to the global fairness verification of tree-based classifiers. Given a tree-based classifier and a set of sensitive features potentially leading to discrimination, our analysis synthesizes sufficient conditions for fairness, expressed as a set of traditional propositional logic formulas, which are readily understandable by human experts. The verified fairness guarantees are global, in that the formulas predicate over all the possible inputs of the classifier, rather than just a few specific test instances. Our analysis is formally proved both sound and complete. Experimental results on public datasets show that the analysis is precise, explainable to human experts and efficient enough for practical adoption.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 34 of its 60 references that are in the map. First posted on arXiv 27 Sep 2022.

Builds on

34 of 60 references.

References · 34

  1. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  2. 2013Intriguing properties of neural networkscited by 5,155
  3. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  4. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
  5. 2020Fairness in Machine Learning: A Surveycited by 370
  6. 2020Fairness in Machine Learningcited by 194
  7. 2017Fairness Testing: Testing Software for Discriminationcited by 126
  8. 2021Globally-Robust Neural Networkscited by 108
  9. 2018Automated Directed Fairness Testingcited by 78
  10. 2019Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Makingcited by 75
  11. 2020Learning Certified Individually Fair Representationscited by 69
  12. 2021Counterfactual Explainable Recommendationcited by 60
22 more
  1. 2019FlipTest: Fairness Testing via Optimal Transportcited by 51
  2. 2017Learning Certifiably Optimal Rule Lists for Categorical Datacited by 50
  3. 2020Verifying Individual Fairness in Machine Learning Modelscited by 49
  4. 2018Probabilistic Verification of Fairness Properties via Concentrationcited by 45
  5. 2015Evasion and Hardening of Tree Ensemble Classifierscited by 44
  6. 2019Perfectly Parallel Fairness Certification of Neural Networkscited by 42
  7. 2014Interpreting Tree Ensembles with inTreescited by 37
  8. 2020Explainability for fair machine learningcited by 34
  9. 2020Justicia: A Stochastic SAT Approach to Formally Verify Fairnesscited by 25
  10. 2020A Statistical Test for Probabilistic Fairnesscited by 25
  11. 2020Interpretable Random Forests via Rule Extractioncited by 23
  12. 2022Marrying Fairness and Explainability in Supervised Learningcited by 23
  13. 2020Machine learning fairness notions: Bridging the gap with real-world applicationscited by 19
  14. 2021Probabilistic Verification of Neural Networks Against Group Fairnesscited by 17
  15. 2022CertiFair: A Framework for Certified Global Fairness of Neural Networkscited by 17
  16. 2021Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AIcited by 16
  17. 2022Explainable Fairness in Recommendationcited by 15
  18. 2021Learning Security Classifiers with Verified Global Robustness Propertiescited by 14
  19. 2019Formal Verification of Input-Output Mappings of Tree Ensemblescited by 13
  20. 2021FairCanary: Rapid Continuous Explainable Fairnesscited by 12
  21. 2019Fair-by-design explainable models for prediction of recidivismcited by 8
  22. 2021Beyond Robustness: Resilience Verification of Tree-Based Classifierscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 3.

5 papers.

  1. 2026Data-Aware and Scalable Sensitivity Analysis for Decision Tree Ensemblescited by 2
  2. 2023Search-Based Fairness Testing: An Overviewcited by 0
  3. 2024TA3: Testing Against Adversarial Attacks on Machine Learning Modelscited by 0
  4. 2026Quantifying Sensitivity for Tree Ensembles: A symbolic and compositional approachcited by 0
  5. 2026PINE: Pruning Boosted Tree Ensembles with Conformal In-Distribution Prediction Equivalencecited by 0