mlsanityData to 7 Oct 2026

Verifying Individual Fairness in Machine Learning Models

arXiv 2006.11737 ↗First posted 21 Jun 2020cs.LG cs.AI stat.MLcited by 49

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Abstract (arXiv)

We consider the problem of whether a given decision model, working with structured data, has individual fairness. Following the work of Dwork, a model is individually biased (or unfair) if there is a pair of valid inputs which are close to each other (according to an appropriate metric) but are treated differently by the model (different class label, or large difference in output), and it is unbiased (or fair) if no such pair exists. Our objective is to construct verifiers for proving individual fairness of a given model, and we do so by considering appropriate relaxations of the problem. We construct verifiers which are sound but not complete for linear classifiers, and kernelized polynomial/radial basis function classifiers. We also report the experimental results of evaluating our proposed algorithms on publicly available datasets.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jun 2020.

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Citing papers per year: 2021: 5; 2022: 14; 2023: 12; 2024: 5; 2025: 8; 2026: 5.

49 papers.

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  1. 2022Causality-based Neural Network Repaircited by 44
  2. 2021Fair Normalizing Flowscited by 38
  3. 2021Testing Group Fairness via Optimal Transport Projectionscited by 23
  4. 2022Individual Fairness Guarantees for Neural Networkscited by 23
  5. 2022Fairify: Fairness Verification of Neural Networkscited by 22
  6. 2021Latent Space Smoothing for Individually Fair Representationscited by 18
  7. 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
  8. 2022CertiFair: A Framework for Certified Global Fairness of Neural Networkscited by 17
  9. 2021Algorithmic Fairness Verification with Graphical Modelscited by 16
  10. 2024FairProof : Confidential and Certifiable Fairness for Neural Networkscited by 13
  11. 2023FAIRER: Fairness as Decision Rationale Alignmentcited by 12
  12. 2022Certifying Some Distributional Fairness with Subpopulation Decompositioncited by 11
37 more
  1. 2021Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice modelscited by 10
  2. 2022Learning Antidote Data to Individual Unfairnesscited by 8
  3. 2023Monitoring Algorithmic Fairnesscited by 8
  4. 2023FFPDG: Fast, Fair and Private Data Generationcited by 8
  5. 2022Verifying Fairness in Quantum Machine Learningcited by 7
  6. 2023Monitoring Algorithmic Fairness under Partial Observationscited by 7
  7. 2024Secure and Confidential Certificates of Online Fairnesscited by 7
  8. 2022Private and Reliable Neural Network Inferencecited by 6
  9. 2024Active Fourier Auditor for Estimating Distributional Properties of ML Modelscited by 6
  10. 2022Explainable Global Fairness Verification of Tree-Based Classifierscited by 5
  11. 2022FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms for Neural Networkscited by 4
  12. 2023Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spacescited by 4
  13. 2024FairQuant: Certifying and Quantifying Fairness of Deep Neural Networkscited by 4
  14. 2023Individual Fairness in Bayesian Neural Networkscited by 3
  15. 2023Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustnesscited by 3
  16. 2025FairSense: Long-Term Fairness Analysis of ML-Enabled Systemscited by 3
  17. 2025Monitoring Robustness and Individual Fairnesscited by 3
  18. 2022Plug and Play Counterfactual Text Generation for Model Robustnesscited by 2
  19. 2023To be Robust and to be Fair: Aligning Fairness with Robustnesscited by 2
  20. 2023Certification of Distributional Individual Fairnesscited by 2
  21. 2022fAux: Testing Individual Fairness via Gradient Alignmentcited by 1
  22. 2023Software Doping Analysis for Human Oversightcited by 1
  23. 2024Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learningcited by 1
  24. 2025Uncovering Discrimination Clusters: Quantifying and Explaining Systematic Fairness Violationscited by 1
  25. 2026Auditing Fairness under Model Updates: Fundamental Complexity and Property-Preserving Updatescited by 1
  26. 2022Artificial Intelligence Nomenclature Identified From Delphi Study on Key Issues Related to Trust and Barriers to Adoption for Autonomous Systemscited by 0
  27. 2023FairPilot: An Explorative System for Hyperparameter Tuning through the Lens of Fairnesscited by 0
  28. 2023Verification of Neural Networks Local Differential Classification Privacycited by 0
  29. 2025Language Models That Walk the Talk: A Framework for Formal Fairness Certificatescited by 0
  30. 2025SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encodingcited by 0
  31. 2025Monitoring of Static Fairnesscited by 0
  32. 2025Algorithmic Fairness: A Runtime Perspectivecited by 0
  33. 2025Quantitative Verification of Fairness in Tree Ensemblescited by 0
  34. 2026Quantifying Sensitivity for Tree Ensembles: A symbolic and compositional approachcited by 0
  35. 2026Provable Fairness Repair for Deep Neural Networkscited by 0
  36. 2026Fairness Invariants: A Relational Approach to Explaining and Mitigating Fairness Bugscited by 0
  37. 2026Security Properties of Neural Networks as Decision Problemscited by 0