mlsanityData to 7 Oct 2026

Within-group fairness: A guidance for more sound between-group fairness

arXiv 2301.08375 ↗First posted 20 Jan 2023stat.ML cs.CY cs.LGcited by 1

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Abstract (arXiv)

As they have a vital effect on social decision-making, AI algorithms not only should be accurate and but also should not pose unfairness against certain sensitive groups (e.g., non-white, women). Various specially designed AI algorithms to ensure trained AI models to be fair between sensitive groups have been developed. In this paper, we raise a new issue that between-group fair AI models could treat individuals in a same sensitive group unfairly. We introduce a new concept of fairness so-called within-group fairness which requires that AI models should be fair for those in a same sensitive group as well as those in different sensitive groups. We materialize the concept of within-group fairness by proposing corresponding mathematical definitions and developing learning algorithms to control within-group fairness and between-group fairness simultaneously. Numerical studies show that the proposed learning algorithms improve within-group fairness without sacrificing accuracy as well as between-group fairness.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 43 references that are in the map. First posted on arXiv 20 Jan 2023.

Builds on

21 of 43 references.

References · 21

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2014Certifying and removing disparate impactcited by 715
  4. 2017On Fairness and Calibrationcited by 406
  5. 2017Algorithmic decision making and the cost of fairnesscited by 402
  6. 2020Fairness without Demographics through Adversarially Reweighted Learningcited by 251
  7. 2018Empirical Risk Minimization under Fairness Constraintscited by 226
  8. 2019Flexibly Fair Representation Learning by Disentanglementcited by 196
  9. 2018FairGAN: Fairness-aware Generative Adversarial Networkscited by 164
  10. 2018Classification with Fairness Constraints: A Meta-Algorithm with Provable Guaranteescited by 146
  11. 2020Algorithmic Fairnesscited by 143
  12. 2019Wasserstein Fair Classificationcited by 131
9 more
  1. 2018A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indicescited by 97
  2. 2016A Confidence-Based Approach for Balancing Fairness and Accuracycited by 73
  3. 2016Satisfying Real-world Goals with Dataset Constraintscited by 68
  4. 2019Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classificationcited by 60
  5. 2018FNNC: Achieving Fairness through Neural Networkscited by 43
  6. 2017Penalizing Unfairness in Binary Classificationcited by 25
  7. 2018Fairness-aware Classification: Criterion, Convexity, and Boundscited by 14
  8. 2020Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraintscited by 7
  9. 2019Group-based Fair Learning Leads to Counter-intuitive Predictionscited by 2

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Peer-induced Fairness: A Causal Approach for Algorithmic Fairness Auditingcited by 0