mlsanityData to 7 Oct 2026

Post-processing for Individual Fairness

arXiv 2110.13796 ↗First posted 26 Oct 2021stat.ML cs.LGcited by 61

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Abstract (arXiv)

Post-processing in algorithmic fairness is a versatile approach for correcting bias in ML systems that are already used in production. The main appeal of post-processing is that it avoids expensive retraining. In this work, we propose general post-processing algorithms for individual fairness (IF). We consider a setting where the learner only has access to the predictions of the original model and a similarity graph between individuals, guiding the desired fairness constraints. We cast the IF post-processing problem as a graph smoothing problem corresponding to graph Laplacian regularization that preserves the desired "treat similar individuals similarly" interpretation. Our theoretical results demonstrate the connection of the new objective function to a local relaxation of the original individual fairness. Empirically, our post-processing algorithms correct individual biases in large-scale NLP models such as BERT, while preserving accuracy.

Key facts. Cited by 61 papers in the map (data through 7 Oct 2026). Builds on 18 of its 46 references that are in the map. First posted on arXiv 26 Oct 2021.

Builds on

18 of 46 references.

References · 18

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
  4. 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 911
  5. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  6. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 633
  7. 2019Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Settingcited by 346
  8. 2019Measuring Bias in Contextualized Word Representationscited by 275
  9. 2018Multiaccuracy: Black-Box Post-Processing for Fairness in Classificationcited by 221
  10. 2015Coordinate Descent Algorithmscited by 218
  11. 2019Training individually fair ML models with Sensitive Subspace Robustnesscited by 73
  12. 2018Bias Mitigation Post-processing for Individual and Group Fairnesscited by 72
6 more
  1. 2018iFair: Learning Individually Fair Data Representations for Algorithmic Decision Makingcited by 64
  2. 2020Two Simple Ways to Learn Individual Fairness Metrics from Datacited by 54
  3. 2019Metric Learning for Individual Fairnesscited by 51
  4. 2020SenSeI: Sensitive Set Invariance for Enforcing Individual Fairnesscited by 37
  5. 2021Individually Fair Gradient Boostingcited by 12
  6. 2021Priority-based Post-Processing Bias Mitigation for Individual and Group Fairnesscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 14; 2023: 19; 2024: 11; 2025: 12; 2026: 5.

61 papers.

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  1. 2023Fast Model Debias with Machine Unlearningcited by 70
  2. 2022Fairness Reprogrammingcited by 33
  3. 2022Understanding Instance-Level Impact of Fairness Constraintscited by 27
  4. 2022FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learningcited by 22
  5. 2022FARE: Provably Fair Representation Learning with Practical Certificatescited by 21
  6. 2023Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networkscited by 17
  7. 2022Accurate Fairness: Improving Individual Fairness without Trading Accuracycited by 16
  8. 2022Domain Adaptation meets Individual Fairness. And they get alongcited by 13
  9. 2024The Pursuit of Fairness in Artificial Intelligence Models: A Surveycited by 13
  10. 2024ElecBench: a Power Dispatch Evaluation Benchmark for Large Language Modelscited by 12
  11. 2023Simple Disentanglement of Style and Content in Visual Representationscited by 10
  12. 2023Counterfactually Fair Representationcited by 9
38 more
  1. 2022Learning Antidote Data to Individual Unfairnesscited by 8
  2. 2024Counterfactual Fairness by Combining Factual and Counterfactual Predictionscited by 8
  3. 2025Towards Fair In-Context Learning with Tabular Foundation Modelscited by 8
  4. 2023SimFair: A Unified Framework for Fairness-Aware Multi-Label Classificationcited by 7
  5. 2023Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairnesscited by 7
  6. 2023Disentangling and Operationalizing AI Fairness at LinkedIncited by 7
  7. 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
  8. 2022Metrizing Fairnesscited by 6
  9. 2023GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hintcited by 6
  10. 2022iFlipper: Label Flipping for Individual Fairnesscited by 5
  11. 2022FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Datacited by 5
  12. 2022Fair Wrapping for Black-box Predictionscited by 4
  13. 2022FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms for Neural Networkscited by 4
  14. 2022Fairness in Forecasting of Observations of Linear Dynamical Systemscited by 4
  15. 2023Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairnesscited by 4
  16. 2023Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimizationcited by 4
  17. 2023Holistic Survey of Privacy and Fairness in Machine Learningcited by 4
  18. 2023Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spacescited by 4
  19. 2023Group-blind optimal transport to group parity and its constrained variantscited by 4
  20. 2023Code Search Debiasing:Improve Search Results beyond Overall Ranking Performancecited by 4
  21. 2022Closed-Loop View of the Regulation of AI: Equal Impact across Repeated Interactionscited by 3
  22. 2025FAIR-SIGHT: Fairness Assurance in Image Recognition via Simultaneous Conformal Thresholding and Dynamic Output Repaircited by 3
  23. 2023Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyondcited by 2
  24. 2023Mitigating Label Bias in Machine Learning: Fairness through Confident Learningcited by 2
  25. 2024Multi-Output Distributional Fairness via Post-Processingcited by 2
  26. 2025Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisationcited by 2
  27. 2025FairAgent: Democratizing Fairness-Aware Machine Learning with LLM-Powered Agentscited by 2
  28. 2023On Comparing Fair Classifiers under Data Biascited by 1
  29. 2023Kantian Deontology Meets AI Alignment: Towards Morally Grounded Fairness Metricscited by 1
  30. 2024Fairness Certification for Natural Language Processing and Large Language Modelscited by 1
  31. 2024A Distributionally Robust Optimisation Approach to Fair Credit Scoringcited by 1
  32. 2024On the Power of Randomization in Fair Classification and Representationcited by 1
  33. 2025A Multi-Objective Evaluation Framework for Analyzing Utility-Fairness Trade-Offs in Machine Learning Systemscited by 1
  34. 2025Selecting for Less Discriminatory Algorithms: A Relational Search Framework for Navigating Fairness-Accuracy Trade-offs in Practicecited by 1
  35. 2025'Rich Dad, Poor Lad': How do Large Language Models Contextualize Socioeconomic Factors in College Admission ?cited by 1
  36. 2026Investigating Bias and Fairness in Appearance-based Gaze Estimationcited by 1
  37. 2026Characterizing Readability Issue Patterns and the Role of Prompt Design in LLM-Generated Codecited by 1
  38. 2024Balancing Fairness and Accuracy in Data-Restricted Binary Classificationcited by 0

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