mlsanityData to 7 Oct 2026

A Model Ensemble-Based Post-Processing Framework for Fairness-Aware Prediction

arXiv 2603.18838 ↗First posted 19 Mar 2026cs.LG stat.MLcited by 0

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Abstract (arXiv)

Striking an optimal balance between predictive performance and fairness continues to be a fundamental challenge in machine learning. In this work, we propose a post-processing framework that facilitates fairness-aware prediction by leveraging model ensembling. Designed to operate independently of any specific model internals, our approach is widely applicable across various learning tasks, model architectures, and fairness definitions. Through extensive experiments spanning classification, regression, and survival analysis, we demonstrate that the framework effectively enhances fairness while maintaining, or only minimally affecting, predictive accuracy.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 60 references that are in the map. First posted on arXiv 19 Mar 2026.

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References · 27

  1. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  2. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
  3. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  4. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  5. 2020GShard: Scaling Giant Models with Conditional Computation and Automatic Shardingcited by 1,601
  6. 2021GLaM: Efficient Scaling of Language Models with Mixture-of-Expertscited by 890
  7. 2014Certifying and removing disparate impactcited by 715
  8. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  9. 2018A Reductions Approach to Fair Classificationcited by 585
  10. 2017On Fairness and Calibrationcited by 406
  11. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 381
  12. 2020Fairness in Machine Learning: A Surveycited by 369
15 more
  1. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  2. 2018Fair Clustering Through Fairletscited by 176
  3. 2019Fair Regression: Quantitative Definitions and Reduction-based Algorithmscited by 168
  4. 2017A Convex Framework for Fair Regressioncited by 147
  5. 2019Wasserstein Fair Classificationcited by 131
  6. 2021Post-processing for Individual Fairnesscited by 61
  7. 2022Fair and Optimal Classification via Post-Processingcited by 50
  8. 2023FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methodscited by 33
  9. 2020A Notion of Individual Fairness for Clusteringcited by 20
  10. 2022Beyond Adult and COMPAS: Fairness in Multi-Class Predictioncited by 16
  11. 2023FRAPPE: A Group Fairness Framework for Post-Processing Everythingcited by 16
  12. 2022Approximate Group Fairness for Clusteringcited by 7
  13. 2022Flexible Group Fairness Metrics for Survival Analysiscited by 4
  14. 2024Post-processing fairness with minimal changescited by 3
  15. 2023Local Law 144: A Critical Analysis of Regression Metricscited by 2

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