mlsanityData to 7 Oct 2026

Fairness via Independence: A (Conditional) Distance Covariance Framework

arXiv 2412.00720 ↗First posted 1 Dec 2024cs.LG cs.CY stat.MLcited by 2

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Abstract (arXiv)

We explore fairness from a statistical perspective by selectively utilizing either conditional distance covariance or distance covariance statistics as measures to assess the independence between predictions and sensitive attributes. We boost fairness with independence by adding a distance covariance-based penalty to the model's training. Additionally, we present the matrix form of empirical (conditional) distance covariance for parallel calculations to enhance computational efficiency. Theoretically, we provide a proof for the convergence between empirical and population (conditional) distance covariance, establishing necessary guarantees for batch computations. Through experiments conducted on a range of real-world datasets, we have demonstrated that our method effectively bridges the fairness gap in machine learning. Our code is available at urlhttps://github.com/liuhaixias1/Fairdc/.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 28 of its 51 references that are in the map. First posted on arXiv 1 Dec 2024.

Builds on

28 of 51 references.

References · 28

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  3. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  4. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  5. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  6. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  7. 2019On Variational Bounds of Mutual Informationcited by 550
  8. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 443
  9. 2017Age Progression/Regression by Conditional Adversarial Autoencodercited by 426
  10. 2017On Fairness and Calibrationcited by 406
  11. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  12. 2018Learning Not to Learn: Training Deep Neural Networks with Biased Datacited by 255
16 more
  1. 2020Fairness without Demographics through Adversarially Reweighted Learningcited by 251
  2. 2019Flexibly Fair Representation Learning by Disentanglementcited by 196
  3. 2018FairGAN: Fairness-aware Generative Adversarial Networkscited by 164
  4. 2020Fair Attribute Classification through Latent Space De-biasingcited by 125
  5. 2018Invariant Representations without Adversarial Trainingcited by 111
  6. 2021Fair Mixup: Fairness via Interpolationcited by 109
  7. 2018Learning Controllable Fair Representationscited by 83
  8. 2022Fair Contrastive Learning for Facial Attribute Classificationcited by 71
  9. 2021Controllable Guarantees for Fair Outcomes via Contrastive Information Estimationcited by 45
  10. 2017Fair Kernel Learningcited by 39
  11. 2021Contrastive Learning for Fair Representationscited by 34
  12. 2023FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methodscited by 33
  13. 2021A Stochastic Optimization Framework for Fair Risk Minimizationcited by 26
  14. 2020Fair Meta-Learning For Few-Shot Classificationcited by 13
  15. 2017Optimized Data Pre-Processing for Discrimination Preventioncited by 12
  16. 2021Attributing Fair Decisions with Attention Interventionscited by 10

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Citing papers per year: 2025: 2.

2 papers.

  1. 2025Fairness Overfitting in Machine Learning: An Information-Theoretic Perspectivecited by 3
  2. 2025Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisationcited by 2