mlsanityData to 7 Oct 2026

Fairness Risks for Group-conditionally Missing Demographics

arXiv 2402.13393 ↗First posted 20 Feb 2024cs.LG cs.CYcited by 0

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Abstract (arXiv)

Fairness-aware classification models have gained increasing attention in recent years as concerns grow on discrimination against some demographic groups. Most existing models require full knowledge of the sensitive features, which can be impractical due to privacy, legal issues, and an individual's fear of discrimination. The key challenge we will address is the group dependency of the unavailability, e.g., people of some age range may be more reluctant to reveal their age. Our solution augments general fairness risks with probabilistic imputations of the sensitive features, while jointly learning the group-conditionally missing probabilities in a variational auto-encoder. Our model is demonstrated effective on both image and tabular datasets, achieving an improved balance between accuracy and fairness.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 55 references that are in the map. First posted on arXiv 20 Feb 2024.

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31 of 55 references.

References · 31

  1. 2014Deep Learning Face Attributes in the Wildcited by 3,679
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  3. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
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  5. 2014Certifying and removing disparate impactcited by 715
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  11. 2020Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Informationcited by 168
  12. 2018Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobservedcited by 94
19 more
  1. 2019Fairness risk measurescited by 85
  2. 2019Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combinationcited by 79
  3. 2020Robust Optimization for Fairness with Noisy Protected Groupscited by 78
  4. 2017Learning Disentangled Representations with Semi-Supervised Deep Generative Modelscited by 72
  5. 2021Fair Feature Distillation for Visual Recognitioncited by 59
  6. 2020Fair Learning with Private Demographic Datacited by 46
  7. 2021Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Featurescited by 45
  8. 2019Noise-tolerant fair classificationcited by 40
  9. 2021Learning Fair Classifiers with Partially Annotated Group Labelscited by 40
  10. 2018Proxy Fairnesscited by 29
  11. 2021Assessing Fairness in the Presence of Missing Datacited by 24
  12. 2021Fair Classification with Adversarial Perturbationscited by 23
  13. 2021Group-Aware Threshold Adaptation for Fair Classificationcited by 21
  14. 2022Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributescited by 17
  15. 2020Unbiased Subdata Selection for Fair Classification: A Unified Framework and Scalable Algorithmscited by 9
  16. 2023Latent Class-Conditional Noise Modelcited by 9
  17. 2023Learning Optimal Fair Scoring Systems for Multi-Class Classificationcited by 8
  18. 2022Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approachcited by 6
  19. 2023Group Fairness with Uncertainty in Sensitive Attributescited by 2

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