mlsanityData to 14 Oct 2026

Beyond Adult and COMPAS: Fairness in Multi-Class Prediction

arXiv 2206.07801 ↗First posted 15 Jun 2022cs.LG cs.CY cs.ITcited by 16

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Abstract (arXiv)

We consider the problem of producing fair probabilistic classifiers for multi-class classification tasks. We formulate this problem in terms of "projecting" a pre-trained (and potentially unfair) classifier onto the set of models that satisfy target group-fairness requirements. The new, projected model is given by post-processing the outputs of the pre-trained classifier by a multiplicative factor. We provide a parallelizable iterative algorithm for computing the projected classifier and derive both sample complexity and convergence guarantees. Comprehensive numerical comparisons with state-of-the-art benchmarks demonstrate that our approach maintains competitive performance in terms of accuracy-fairness trade-off curves, while achieving favorable runtime on large datasets. We also evaluate our method at scale on an open dataset with multiple classes, multiple intersectional protected groups, and over 1M samples.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 26 of its 60 references that are in the map. First posted on arXiv 15 Jun 2022.

Builds on

26 of 60 references.

References · 26

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  2. 2018Datasheets for Datasetscited by 1,386
  3. 2014Certifying and removing disparate impactcited by 715
  4. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
  5. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  6. 2018A Reductions Approach to Fair Classificationcited by 587
  7. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 449
  8. 2017On Fairness and Calibrationcited by 406
  9. 2017Algorithmic decision making and the cost of fairnesscited by 403
  10. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  11. 2018Empirical Risk Minimization under Fairness Constraintscited by 226
  12. 2018A comparative study of fairness-enhancing interventions in machine learningcited by 208
14 more
  1. 2018Classification with Fairness Constraints: A Meta-Algorithm with Provable Guaranteescited by 146
  2. 2019Identifying and Correcting Label Bias in Machine Learningcited by 146
  3. 2017On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learningcited by 142
  4. 2018Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goalscited by 103
  5. 2019Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Makingcited by 75
  6. 2021It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarkscited by 61
  7. 2019Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classificationcited by 60
  8. 2021Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Valuescited by 24
  9. 2021Group-Aware Threshold Adaptation for Fair Classificationcited by 21
  10. 2018Envy-Free Classificationcited by 11
  11. 2020Unbiased Subdata Selection for Fair Classification: A Unified Framework and Scalable Algorithmscited by 9
  12. 2020Fair for All: Best-effort Fairness Guarantees for Classificationcited by 8
  13. 2019Optimized Score Transformation for Consistent Fair Classificationcited by 7
  14. 2015A Note on the Convergence of ADMM for Linearly Constrained Convex Optimization Problemscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 5; 2024: 3; 2025: 5; 2026: 1.

16 papers.

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  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2024CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?cited by 35
  3. 2023FRAPPE: A Group Fairness Framework for Post-Processing Everythingcited by 16
  4. 2022Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utilitycited by 12
  5. 2025KQ-SVD: Compressing the KV Cache with Provable Guarantees on Attention Fidelitycited by 10
  6. 2023Learning Optimal Fair Scoring Systems for Multi-Class Classificationcited by 8
  7. 2025SQuat: Subspace-orthogonal KV Cache Quantizationcited by 7
  8. 2023Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groupscited by 6
  9. 2024Post-processing fairness with minimal changescited by 3
  10. 2023On the Robustness of Text Vectorizerscited by 2
  11. 2023Bias Mitigation Methods for Binary Classification Decision-Making Systems: Survey and Recommendationscited by 1
  12. 2025Learning Pareto manifolds in high dimensions: How can regularization help?cited by 1
4 more
  1. 2024Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approachcited by 0
  2. 2025Controlled Model Debiasing through Minimal and Interpretable Updatescited by 0
  3. 2025Toward Unifying Group Fairness Evaluation from a Sparsity Perspectivecited by 0
  4. 2026A Model Ensemble-Based Post-Processing Framework for Fairness-Aware Predictioncited by 0