mlsanityData to 14 Oct 2026

Set to Be Fair: Demographic Parity Constraints for Set-Valued Classification

arXiv 2510.04926 ↗First posted 6 Oct 2025stat.ML cs.LGcited by 0

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Abstract (arXiv)

Set-valued classification is used in multiclass settings where confusion between classes can occur and lead to misleading predictions. However, its application may amplify discriminatory bias motivating the development of set-valued approaches under fairness constraints. In this paper, we address the problem of set-valued classification under demographic parity and expected size constraints. We propose two complementary strategies: an oracle-based method that minimizes classification risk while satisfying both constraints, and a computationally efficient proxy that prioritizes constraint satisfaction. For both strategies, we derive closed-form expressions for the (optimal) fair set-valued classifiers and use these to build plug-in, data-driven procedures for empirical predictions. We establish distribution-free convergence rates for violations of the size and fairness constraints for both methods, and under mild assumptions we also provide excess-risk bounds for the oracle-based approach. Empirical results demonstrate the effectiveness of both strategies and highlight the efficiency of our proxy method.

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References · 18

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  2. 2014Certifying and removing disparate impactcited by 715
  3. 2019Conformalized Quantile Regressioncited by 629
  4. 2018A Reductions Approach to Fair Classificationcited by 587
  5. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 369
  6. 2016Least Ambiguous Set-Valued Classifiers with Bounded Error Levelscited by 272
  7. 2023Conformal Prediction With Conditional Guaranteescited by 152
  8. 2019Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classificationcited by 60
  9. 2018How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGDcited by 57
  10. 2015Loss Functions for Top-k Error: Analysis and Insightscited by 38
  11. 2016A statistical framework for fair predictive algorithmscited by 38
  12. 2021Set-valued classification -- overview via a unified frameworkcited by 27
6 more
  1. 2022Fair learning with Wasserstein barycenters for non-decomposable performance measurescited by 24
  2. 2022Conformalized Fairness via Quantile Regressioncited by 19
  3. 2022Survey on Fairness Notions and Related Tensionscited by 9
  4. 2024Conformal Classification with Equalized Coverage for Adaptively Selected Groupscited by 9
  5. 2024Multimodal Gender Fairness in Depression Prediction: Insights on Data from the USA & Chinacited by 7
  6. 2018Can everyday AI be ethical. Fairness of Machine Learning Algorithmscited by 1

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