mlsanityData to 7 Oct 2026

Consistent Range Approximation for Fair Predictive Modeling

arXiv 2212.10839 ↗First posted 21 Dec 2022cs.LG cs.AI cs.DBcited by 7

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Abstract (arXiv)

This paper proposes a novel framework for certifying the fairness of predictive models trained on biased data. It draws from query answering for incomplete and inconsistent databases to formulate the problem of consistent range approximation (CRA) of fairness queries for a predictive model on a target population. The framework employs background knowledge of the data collection process and biased data, working with or without limited statistics about the target population, to compute a range of answers for fairness queries. Using CRA, the framework builds predictive models that are certifiably fair on the target population, regardless of the availability of external data during training. The framework's efficacy is demonstrated through evaluations on real data, showing substantial improvement over existing state-of-the-art methods.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 44 of its 104 references that are in the map. First posted on arXiv 21 Dec 2022.

Builds on

44 of 104 references.

References · 44

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  3. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  4. 2014Certifying and removing disparate impactcited by 715
  5. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  6. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
  7. 2017On Fairness and Calibrationcited by 406
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  9. 2020Fairness in Machine Learning: A Surveycited by 369
  10. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  11. 2018Learning Adversarially Fair and Transferable Representationscited by 353
  12. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
32 more
  1. 2017Avoiding Discrimination through Causal Reasoningcited by 191
  2. 2017A Convex Framework for Fair Regressioncited by 147
  3. 2017Fair Inference On Outcomescited by 143
  4. 2021Fairness in Credit Scoring: Assessment, Implementation and Profit Implicationscited by 65
  5. 2020Robust Fairness under Covariate Shiftcited by 58
  6. 2019Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?cited by 50
  7. 2018Residual Unfairness in Fair Machine Learning from Prejudiced Datacited by 49
  8. 2020A Distributionally Robust Approach to Fair Classificationcited by 43
  9. 2022Representation Bias in Data: A Survey on Identification and Resolution Techniquescited by 37
  10. 2019Transfer of Machine Learning Fairness across Domainscited by 36
  11. 2022Fairness Transferability Subject to Bounded Distribution Shiftcited by 28
  12. 2017Penalizing Unfairness in Binary Classificationcited by 25
  13. 2020Causal Feature Selection for Algorithmic Fairnesscited by 25
  14. 2020Does enforcing fairness mitigate biases caused by subpopulation shift?cited by 25
  15. 2018Training Set Debugging Using Trusted Itemscited by 24
  16. 2021Assessing Fairness in the Presence of Missing Datacited by 24
  17. 2021Certifying Robustness to Programmable Data Bias in Decision Treescited by 21
  18. 2021OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learningcited by 18
  19. 2020The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspectivecited by 17
  20. 2020The Impossibility Theorem of Machine Fairness -- A Causal Perspectivecited by 13
  21. 2021Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classificationcited by 13
  22. 2022Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settingscited by 11
  23. 2021Wasserstein Robust Classification with Fairness Constraintscited by 10
  24. 2022Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairnesscited by 10
  25. 2021Robust Fairness-aware Learning Under Sample Selection Biascited by 9
  26. 2021Fairness Through Regularization for Learning to Rankcited by 8
  27. 2019Capuchin: Causal Database Repair for Algorithmic Fairnesscited by 6
  28. 2022Algorithmic decision making methods for fair credit scoringcited by 5
  29. 2019Mosaic: A Sample-Based Database System for Open World Query Processingcited by 2
  30. 2020Sample Debiasing in the Themis Open World Database System (Extended Version)cited by 2
  31. 2022A Survey on Preserving Fairness Guarantees in Changing Environmentscited by 2
  32. 2021Consistent Answers of Aggregation Queries using SAT Solverscited by 1

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Cited by year

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

7 papers.

  1. 2023Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approachcited by 12
  2. 2023How Far Can Fairness Constraints Help Recover From Biased Data?cited by 5
  3. 2025Causal DAG Summarization (Full Version)cited by 2
  4. 2023On Comparing Fair Classifiers under Data Biascited by 1
  5. 2025Unbiased Binning for Fairness-aware Attribute Representationcited by 1
  6. 2024Learning from Uncertain Data: From Possible Worlds to Possible Modelscited by 0
  7. 2025Stress-Testing Causal Claims via Cardinality Repairscited by 0