FairGAN: Fairness-aware Generative Adversarial Networks
Open accessAbstract
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.
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There has been work proposing the use of an adversary as a pre-processing transformation process on the training data with similar objectives to transformation yet often moving towards a notion of censoring the training data with similar objectives to variable blinding.
“There has also been work proposing the use of an adversary as a pre-processing transformation process on the training data (e.g.[1,99,162,206,234,299,300]) with similar objectives to transformation as discussed in subsection 4.4 yet often moving towards a notion of łcensoringž the training data with similar objectives to variable blinding as discussed in subsection 4.1.”
From Fairness in machine learning: A survey (2024) · cites this paper as [300]
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- Authors
- Xu, D., Yuan, S., Zhang, L., & Wu, X.
- Affiliations
- University of Arkansas at Fayetteville (US)
- Venue
- 2018 IEEE International Conference on Big Data (Big Data)
- Publisher
- IEEE
- Published
- 2018-12
- Pages
- 570-575
- OpenAlex
- W2963290659
- Cited by
- 379
- References
- 25
- Citations by year
- 2026: 28 · 2025: 56 · 2024: 61 · 2023: 75 · 2022: 63 · 2021: 49 · 2020: 29 · 2019: 18
- FWCI
- 42.96
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Big Data (BigData) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1564250, 1646654)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- discrimination prevention, generative models, data utility preservation, fair classification, synthetic data
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