Fairness GAN: Generating datasets with fairness properties using a generative adversarial network

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Unpaired image-to-image translation using cycle-consistent adversarial networks

Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602

Eigenfaces for Recognition

Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733

Deep Learning Face Attributes in the Wild

Liu, Z., Luo, P., Wang, X., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.425 · Cited by 7,747

Improved training of Wasserstein GANs

Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017) · arXiv · doi:10.48550/arxiv.1704.00028 · Cited by 6,800

Categorical Reparameterization with Gumbel-Softmax

Jang, E., Gu, S., & Poole, B. (2016) · arXiv · doi:10.48550/arxiv.1611.01144 · Cited by 3,616

Spectral Normalization for Generative Adversarial Networks

Miyato, T., Kataoka, T., Koyama, M., & Yoshida, Y. (2018) · arXiv · doi:10.48550/arxiv.1802.05957 · Cited by 3,438

Conditional Image Synthesis With Auxiliary Classifier GANsPreprint

Odena, A., Olah, C., & Shlens, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.09585 · Cited by 3,030

The Concrete Distribution: A Continuous Relaxation of Discrete Random VariablesPreprint

Maddison, C. J., Mnih, A., & Teh, Y. W. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.00712 · Cited by 2,170

NIPS 2016 Tutorial: Generative Adversarial NetworksPreprint

Goodfellow, I. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.00160 · Cited by 2,048

Mitigating Unwanted Biases with Adversarial Learning

Zhang, B. H., Lemoine, B., & Mitchell, M. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3278721.3278779 · Cited by 1,387

Data preprocessing techniques for classification without discrimination

Kamiran, F., & Calders, T. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-011-0463-8 · Cited by 1,305

A Learned Representation For Artistic StylePreprint

Dumoulin, V., Shlens, J., & Kudlur, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.07629 · Cited by 1,114

Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results

Silberzahn, R., Uhlmann, E. L., Martin, D. P., Anselmi, P., Aust, F., Awtrey, E. C., Bahník, Š., Bai, F., Bannard, C., Bonnier, E., et al. (2018) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245917747646 · Cited by 773

cGANs with Projection DiscriminatorPreprint

Miyato, T., & Koyama, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.05637 · Cited by 619

A comparative study of fairness-enhancing interventions in machine learning

Friedler, S. A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E. P., & Roth, D. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287589 · Cited by 601

Invertible Conditional GANs for image editing

Perarnau, G., van de Weijer, J., Raducanu, B., & Álvarez, J. M. (2016) · arXiv · doi:10.48550/arxiv.1611.06355 · Cited by 563

Censoring Representations with an AdversaryPreprint

Edwards, H., & Storkey, A. (2015) · International Conference on Learning Representations · doi:10.48550/arxiv.1511.05897 · Cited by 563

Learning Adversarially Fair and Transferable RepresentationsPreprint

Madras, D., Creager, E., Pitassi, T., & Zemel, R. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.06309 · Cited by 479

FairGAN: Fairness-aware Generative Adversarial Networks

Xu, D., Yuan, S., Zhang, L., & Wu, X. (2018) · 2018 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata.2018.8622525 · Cited by 379

Adversarial Removal of Demographic Attributes from Text Data

Elazar, Y., & Goldberg, Y. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1002 · Cited by 307

Data Decisions and Theoretical Implications when Adversarially Learning Fair RepresentationsPreprint

Beutel, A., Chen, J., Zhao, Z., & Chi, E. H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.00075 · Cited by 282

Learning to Pivot with Adversarial NetworksPreprint

Louppe, G., Kagan, M., & Cranmer, K. (2016) · Neural Information Processing Systems · doi:10.48550/arxiv.1611.01046 · Cited by 250

State-of-the-art automated essay scoring: Competition, results, and future directions from a United States demonstration

Shermis, M. D. (2014) · Assessing Writing · doi:10.1016/j.asw.2013.04.001 · Cited by 227

How Algorithms Discriminate Based on Data They Lack: Challenges, Solutions, and Policy Implications

Williams, B. A., Brooks, C., & Shmargad, Y. (2018) · Journal of Information Policy · doi:10.5325/jinfopoli.8.2018.0078 · Cited by 224

Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification

d'Alessandro, B., O'Neil, C., & LaGatta, T. (2017) · Big Data · doi:10.1089/big.2016.0048 · Cited by 195

When “the state of the art” is counting words

Perelman, L. (2014) · Assessing Writing · doi:10.1016/j.asw.2014.05.001 · Cited by 139

Achieving Fairness through Adversarial Learning: an Application to Recidivism PredictionPreprint

Wadsworth, C., Vera, F., & Piech, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.00199 · Cited by 139

One-Network Adversarial Fairness

Adel, T., Valera, I., Ghahramani, Z., & Weller, A. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33012412 · Cited by 95

Conditional CycleGAN for Attribute Guided Face Image GenerationPreprint

Lu, Y., Tai, Y., & Tang, C. (2017) · arXiv (Cornell University) · Cited by 57

Server, server in the cloud. Who is the fairest in the crowd?Preprint

Böhlen, M., Chandola, V., & Salunkhe, A. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.08801 · Cited by 5

Obstructing Classification via Projection

Haghighatkhah, P., Meulemans, W., Speckmann, B., Urhausen, J., & Verbeek, K. (2021) · TU/e Research Portal · doi:10.4230/lipics.mfcs.2021.56 · Cited by 2

Algorithmic fairness in artificial intelligence for medicine and healthcare

Chen, R. J., Wang, J. J., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Lu, M. Y., Sahai, S., & Mahmood, F. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01056-8 · Cited by 750

Fairness in machine learning: A survey

Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646

A Review on Fairness in Machine Learning

Pessach, D., & Shmueli, E. (2023) · ACM Computing Surveys · doi:10.1145/3494672 · Cited by 598

Pros and cons of GAN evaluation measures: New developments

Borji, A. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103329 · Cited by 381

Socially Responsible AI Algorithms: Issues, Purposes, and Challenges

Cheng, L., Varshney, K. R., & Liu, H. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12814 · Cited by 261

Fairness GANPreprint

Sattigeri, P., Hoffman, S. C., Chenthamarakshan, V., & Varshney, K. R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09910 · Cited by 27

FairGAN+: Achieving Fair Data Generation and Classification through Generative Adversarial Nets

Xu, D., Yuan, S., Zhang, L., & Wu, X. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006322 · Cited by 32

FairGAN: Fairness-aware Generative Adversarial Networks

Xu, D., Yuan, S., Zhang, L., & Wu, X. (2018) · 2018 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata.2018.8622525 · Cited by 379

Distance Correlation GAN: Fair Tabular Data Generation with Generative Adversarial Networks

Rajabi, A., & Garibay, Ö. Ö. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35891-3_26 · Cited by 4

Fair Data Generation and Machine Learning Through Generative Adversarial Networks

Wu, X., Xu, D., Yuan, S., & Zhang, L. (2022) · Intelligent systems reference library · doi:10.1007/978-3-030-91390-8_3 · Cited by 7