mlsanityData to 7 Oct 2026

Unprocessing Seven Years of Algorithmic Fairness

arXiv 2306.07261 ↗First posted 12 Jun 2023cs.LG cs.CYcited by 18

Not on the map. Why?

Abstract (arXiv)

Seven years ago, researchers proposed a postprocessing method to equalize the error rates of a model across different demographic groups. The work launched hundreds of papers purporting to improve over the postprocessing baseline. We empirically evaluate these claims through thousands of model evaluations on several tabular datasets. We find that the fairness-accuracy Pareto frontier achieved by postprocessing contains all other methods we were feasibly able to evaluate. In doing so, we address two common methodological errors that have confounded previous observations. One relates to the comparison of methods with different unconstrained base models. The other concerns methods achieving different levels of constraint relaxation. At the heart of our study is a simple idea we call unprocessing that roughly corresponds to the inverse of postprocessing. Unprocessing allows for a direct comparison of methods using different underlying models and levels of relaxation.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 24 of its 46 references that are in the map. First posted on arXiv 12 Jun 2023.

Builds on

24 of 46 references.

References · 24

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 633
  3. 2018A Reductions Approach to Fair Classificationcited by 585
  4. 2021Tabular Data: Deep Learning is Not All You Needcited by 530
  5. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 443
  6. 2017Algorithmic decision making and the cost of fairnesscited by 402
  7. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  8. 2020A Metric Learning Reality Checkcited by 317
  9. 2020Environment Inference for Invariant Learningcited by 306
  10. 2017Are GANs Created Equal? A Large-Scale Studycited by 267
  11. 2018Empirical Risk Minimization under Fairness Constraintscited by 226
  12. 2019Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approachescited by 181
12 more
  1. 2018Classification with Fairness Constraints: A Meta-Algorithm with Provable Guaranteescited by 146
  2. 2018Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goalscited by 103
  3. 201850 Years of Test (Un)fairness: Lessons for Machine Learningcited by 101
  4. 2021It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarkscited by 61
  5. 2022Algorithmic Fairness Datasets: the Story so Farcited by 51
  6. 2020Fair Bayesian Optimizationcited by 45
  7. 2022FairGBM: Gradient Boosting with Fairness Constraintscited by 25
  8. 2019ProPublica's COMPAS Data Revisitedcited by 24
  9. 2021Group-Aware Threshold Adaptation for Fair Classificationcited by 21
  10. 2021Promoting Fairness through Hyperparameter Optimizationcited by 17
  11. 2023Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoMLcited by 16
  12. 2023Fairness Improves Learning from Noisily Labeled Long-Tailed Datacited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 8; 2025: 7; 2026: 2.

18 papers.

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  1. 2023Bias Testing and Mitigation in LLM-based Code Generationcited by 53
  2. 2024OxonFair: A Flexible Toolkit for Algorithmic Fairnesscited by 12
  3. 2024ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methodscited by 8
  4. 2024A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problemscited by 5
  5. 2025Overcoming Fairness Trade-offs via Pre-processing: A Causal Perspectivecited by 5
  6. 2024The Costs of Pretending That There Are Data-Generating Probability Distributions in the Social Worldcited by 4
  7. 2024Post-processing fairness with minimal changescited by 3
  8. 2025Minority Collective Action for User-Side Fairnesscited by 3
  9. 2024The Disparate Benefits of Deep Ensemblescited by 2
  10. 2025Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyondcited by 2
  11. 2025Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphscited by 2
  12. 2025Bias Begins with Data: The FairGround Corpus for Robust and Reproducible Research on Algorithmic Fairnesscited by 2
6 more
  1. 2024FROC: Building Fair ROC from a Trained Classifiercited by 1
  2. 2025Reconsidering Fairness Through Unawareness From the Perspective of Model Multiplicitycited by 1
  3. 2024Back to the Drawing Board for Fair Representation Learningcited by 0
  4. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  5. 2026LLM-Augmented Computational Phenotyping of Long Covidcited by 0
  6. 2026FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Datacited by 0