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FairGAN: Fairness-aware Generative Adversarial Networks

arXiv 1805.11202 ↗First posted 28 May 2018cs.LG cs.CY stat.MLcited by 164

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Abstract (arXiv)

Fairness-aware learning is increasingly important in data mining. Discrimination prevention aims to prevent discrimination in the training data before it is used to conduct predictive analysis. In this paper, we focus on fair data generation that ensures the generated data is discrimination free. Inspired by generative adversarial networks (GAN), we present fairness-aware generative adversarial networks, called FairGAN, which are able to learn a generator producing fair data and also preserving good data utility. Compared with the naive fair data generation models, FairGAN further ensures the classifiers which are trained on generated data can achieve fair classification on real data. Experiments on a real dataset show the effectiveness of FairGAN.

Key facts. Cited by 164 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 May 2018.

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Citing papers per year: 2021: 25; 2022: 39; 2023: 37; 2024: 27; 2025: 19; 2026: 17.

164 papers.

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  1. 2023Fair Diffusion: Instructing Text-to-Image Generation Models on Fairnesscited by 149
  2. 2023Debiasing Vision-Language Models via Biased Promptscited by 147
  3. 2022StyleGAN-Human: A Data-Centric Odyssey of Human Generationcited by 143
  4. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  5. 2021Pros and Cons of GAN Evaluation Measures: New Developmentscited by 121
  6. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
  7. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
  8. 2021DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networkscited by 109
  9. 2021Trustworthy AI: A Computational Perspectivecited by 93
  10. 2023Synthcity: facilitating innovative use cases of synthetic data in different data modalitiescited by 76
  11. 2024OpenBias: Open-set Bias Detection in Text-to-Image Generative Modelscited by 61
  12. 2021Rebooting ACGAN: Auxiliary Classifier GANs with Stable Trainingcited by 57
38 more
  1. 2024Balancing Act: Distribution-Guided Debiasing in Diffusion Modelscited by 50
  2. 2022Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Modelscited by 47
  3. 2021Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Featurescited by 45
  4. 2022Unbiased Supervised Contrastive Learningcited by 40
  5. 2022Representation Bias in Data: A Survey on Identification and Resolution Techniquescited by 37
  6. 2021TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networkscited by 34
  7. 2022Fairness Reprogrammingcited by 33
  8. 2021Fairness for Image Generation with Uncertain Sensitive Attributescited by 31
  9. 2021Measuring Fairness in Generative Modelscited by 31
  10. 2023Fair Text-to-Image Diffusion via Fair Mappingcited by 31
  11. 2024Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectivescited by 30
  12. 2021Copyright in Generative Deep Learningcited by 29
  13. 2022Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesiscited by 29
  14. 2023Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Datacited by 29
  15. 2023Synthetic data, real errors: how (not) to publish and use synthetic datacited by 29
  16. 2022Fair Generative Models via Transfer Learningcited by 27
  17. 2021Estimating and Improving Fairness with Adversarial Learningcited by 26
  18. 2022Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refittingcited by 26
  19. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  20. 2023Survey of Social Bias in Vision-Language Modelscited by 26
  21. 2024Training Unbiased Diffusion Models From Biased Datasetcited by 25
  22. 2022Bayes-Optimal Classifiers under Group Fairnesscited by 24
  23. 2022Discovering Transferable Forensic Features for CNN-generated Images Detectioncited by 24
  24. 2022Optimising Equal Opportunity Fairness in Model Trainingcited by 22
  25. 2021Algorithm Fairness in AI for Medicine and Healthcarecited by 21
  26. 2023From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modelingcited by 21
  27. 2022Learning fair representation with a parametric integral probability metriccited by 20
  28. 2023FLAC: Fairness-Aware Representation Learning by Suppressing Attribute-Class Associationscited by 18
  29. 2022CertiFair: A Framework for Certified Global Fairness of Neural Networkscited by 17
  30. 2022Fair Bayes-Optimal Classifiers Under Predictive Paritycited by 16
  31. 2021Data synthesis and adversarial networks: A review and meta-analysis in cancer imagingcited by 15
  32. 2024Multilingual Text-to-Image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help Youcited by 15
  33. 2021On the Fairness of Generative Adversarial Networks (GANs)cited by 14
  34. 2023Improving Fairness using Vision-Language Driven Image Augmentationcited by 13
  35. 2021The Devil is in the GAN: Backdoor Attacks and Defenses in Deep Generative Modelscited by 12
  36. 2022Towards a multi-stakeholder value-based assessment framework for algorithmic systemscited by 12
  37. 2022Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Makingcited by 12
  38. 2023CuTS: Customizable Tabular Synthetic Data Generationcited by 12

50 most cited of 164 All 164 (JSON)