mlsanityData to 7 Oct 2026

Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks

arXiv 2405.02225 ↗First posted 3 May 2024stat.ML cs.AI cs.CYcited by 13

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Abstract (arXiv)

This paper introduces a framework for post-processing machine learning models so that their predictions satisfy multi-group fairness guarantees. Based on the celebrated notion of multicalibration, we introduce (s,G, α)-GMC (Generalized Multi-Dimensional Multicalibration) for multi-dimensional mappings s, constraint set G, and a pre-specified threshold level α. We propose associated algorithms to achieve this notion in general settings. This framework is then applied to diverse scenarios encompassing different fairness concerns, including false negative rate control in image segmentation, prediction set conditional uncertainty quantification in hierarchical classification, and de-biased text generation in language models. We conduct numerical studies on several datasets and tasks.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 28 of its 42 references that are in the map. First posted on arXiv 3 May 2024.

Builds on

28 of 42 references.

References · 28

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2022Training Compute-Optimal Large Language Modelscited by 2,927
  3. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  4. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  5. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 777
  6. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
  7. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
  8. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 381
  9. 2021Towards Understanding and Mitigating Social Biases in Language Modelscited by 294
  10. 2019Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Themcited by 268
  11. 2018Multiaccuracy: Black-Box Post-Processing for Fairness in Classificationcited by 221
  12. 2018Gender Bias in Neural Natural Language Processingcited by 210
16 more
  1. 2017HDLTex: Hierarchical Deep Learning for Text Classificationcited by 105
  2. 2018An Empirical Study of Rich Subgroup Fairness for Machine Learningcited by 81
  3. 2021Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibrationcited by 79
  4. 2020Outcome Indistinguishabilitycited by 74
  5. 2022Practical Adversarial Multivalid Conformal Predictioncited by 65
  6. 2020Moment Multicalibration for Uncertainty Estimationcited by 63
  7. 2021Online Multivalid Learning: Means, Moments, and Prediction Intervalscited by 48
  8. 2022Low-Degree Multicalibrationcited by 44
  9. 2021Multi-group Agnostic PAC Learnabilitycited by 42
  10. 2023High-Dimensional Prediction for Sequential Decision Makingcited by 41
  11. 2019Advancing subgroup fairness via sleeping expertscited by 28
  12. 2021Simple and near-optimal algorithms for hidden stratification and multi-group learningcited by 26
  13. 2017Calibration for the (Computationally-Identifiable) Massescited by 23
  14. 2022Smooth Calibration, Leaky Forecasts, Finite Recall, and Nash Dynamicscited by 22
  15. 2022An Algorithmic Framework for Bias Bountiescited by 19
  16. 2023An investigation into the impact of deep learning model choice on sex and race bias in cardiac MR segmentationcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 3; 2026: 6.

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  1. 2024Localized Adaptive Risk Controlcited by 16
  2. 2024When is Multicalibration Post-Processing Necessary?cited by 16
  3. 2024Automatically Adaptive Conformal Risk Controlcited by 16
  4. 2025Discretization-free Multicalibration through Loss Minimization over Tree Ensemblescited by 6
  5. 2025Do Large Language Models (Really) Need Statistical Foundations?cited by 5
  6. 2024FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Predictioncited by 3
  7. 2025Intersectional Divergence: Measuring Fairness in Regressioncited by 1
  8. 2026Heteroskedastic Signals in Budgeted LLM Verification: Structural Heterogeneity Limits Optimization Gainscited by 1
  9. 2026ShakyPrepend: A Multi-Group Learner with Improved Sample Complexitycited by 0
  10. 2026Trustworthy AI: Ensuring Reliability and Accountability from Models to Agentscited by 0
  11. 2026Demystifying the Optimal Fair Classifier in Multi-Class Classificationcited by 0
  12. 2026TRACE-CRC: Trajectory-Adaptive Conformal Risk Control for Multi-Step Channel State Information Predictioncited by 0
1 more
  1. 2026Available Guardrails: Certifying Selective Prediction across ML Systemscited by 0