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

Dealing with Data Bias in Classification: Can Generated Data Ensure Representation and Fairness?

Duong, M. K., & Conrad, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-39831-5_17 · Cited by 2

Analysing Fairness of Privacy-Utility Mobility Models

Zhan, Y., Haddadi, H., & Mashhadi, A. (2023) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3594739.3610676 · Cited by 2

A Survey on Bias Mitigation in Federated Learning

Ude, B., Odeyomi, O. T., Roy, K., & Yuan, X. (2023) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci52147.2023.10372031 · Cited by 1

Wearing myopia glasses on GANs: Mitigating bias for pre-trained Generative Adversarial Networks via online prior perturbation

Chen, Q., Ye, A., Ye, G., & Huang, C. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112133 · Cited by 1

ActivFairNet: A novel framework for mitigating bias in deep learning networks using activation map-based fairness regularization

AbdulQawy, A., Sallam, E. A., & Elkholy, A. (2025) · Egyptian Informatics Journal · doi:10.1016/j.eij.2025.100739 · Cited by 1

CondFairGen A Fair Conditional Generator for Tabular Data via Adaptive Sampling

David, S., & Kotal, A. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3760967 · Cited by 1

FairGen: Enhancing Fairness in Text-to-Image Diffusion Models via Self-Discovering Latent Directions

Jiang, Y.-L., Li, W.-H., Zhang, Y., Cai, M.-H., & Yue, X.-Y. (2024) · IEEE International Conference on Computer Vision · doi:10.1109/iccv51701.2025.01711 · Cited by 10

Addressing Biases in the Texts Using an End-to-End Pipeline Approach

Raza, S., Bashir, S. R., Sneha, S., & Qamar, U. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-37249-0_8 · Cited by 1

Linking convolutional kernel size to generalization bias in face analysis CNNs

Liang, H., Caro, J. O., Maheshri, V., Patel, A. B., & Balakrishnan, G. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00464 · Cited by 1

On the Basis of Sex: A Review of Gender Bias in Machine Learning Applications.Preprint

Feldman, T., & Peake, A. (2021) · arXiv (Cornell University)

AU-GAN: Attention U-Net Based on a Built-In Attention for Multi-domain Image-to-Image Translation

Xu, C., Jin, G., Wu, M., & Ni, D. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-1354-1_18

Research on an Improved QUIC Protocol MINQUIC

Sun, L. (2023) · International Conference on Big Data, Information and Computer Network (BDICN) · doi:10.1109/bdicn58493.2023.00041

DECODING SEMIOTIC PUZZLES: MANIFEST TV SERIAL, STRUCTURAL IMPLICATIONS AND POSTHUMAN PROTAGONISTS

Yılmazkol, Ö., & Pembecioglu, N. E. (2023) · European Journal of Literature Language and Linguistics Studies · doi:10.46827/ejlll.v7i2.460

Fair Machine Learning Through the Lens of Causality

Wu, Y., Zhang, L., & Wu, X. (2023) · Springer eBooks · doi:10.1007/978-3-031-35051-1_6

Global min-max Computation for α-Hölder Games

Delage, A., Buffet, O., & Dibangoye, J. (2023) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai59109.2023.00083

Unsupervised Learning of Unbiased Visual Representations

Barbano, C. A., Tartaglione, E., & Grangetto, M. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3514554

Idempotence-Constrained Representation Learning: Balancing Sensitive Information Elimination and Feature Robustness

Fan, X., Song, B., Zhang, S., Zhang, P., & Yang, Y. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825539

Exploring Correlated Facial Attributes in Text-to-Image Models: Unintended Consequences in Synthetic Face Generation

Coninck, S. D., Leroux, S., & Simoens, P. (2025) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw65960.2025.00153

Analyzing the Trade-off Between Fairness and Model Performance in Supervised Learning: A Case Study in the MIMIC dataset

Silva, B. P. M., & Berton, L. (2025) · doi:10.5753/sbcas.2025.6994

Responsible Artificial Intelligence for Healthcare: Trends, Challenges, and Opportunities

Baker, S., & Ahn, E. (2025) · Springer eBooks · doi:10.1007/978-3-031-88024-7_1

Samurai and assassins strategy using agent based modelling and simulation for finding success probability and efficiency in complex scenarios

Vincent Gnanaraj, T., & Chenni Kumaran., J. (2025) · Discover Computing · doi:10.1007/s10791-025-09614-1

Mitigating Intersectional Gender and Racial Bias in Sentiment Analysis: A T5-Based Data Augmentation Approach for English and Low-Resource Bengali

Islam, S. (2025) · Journal of Digital Information Management · doi:10.6025/jdim/2025/23/4/203-219

Fairness-aware Machine Learning

Kamishima, T. (2025) · Ouyou toukeigaku · doi:10.5023/jappstat.54.81

Fairness-aware graph representation learning through bias disentanglement

Liu, S., Qin, Z., Hou, X., Wang, Y., Wang, Z., & Peng, Z. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108034

Enhancing Fairness Without Demographic Labels via Identifying and Mitigating Potential Biases

Lee, P., & Park, S. (2026) · Symmetry · doi:10.3390/sym18020344

Fair GANs through model rebalancing for extremely imbalanced class distributions

Jain, A., Memon, N. D., & Togelius, J. (2025) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb65343.2025.11410860

An Adaptive and Explainable GAN Framework for Mitigating Biasing and Fairness in Employee Recruitment and Talent Management

Arora, R., & Damarla, R. B. (2025) · Global AI Summit - International Conference on Artificial Intelligence and Emerging Technology (AI Summit) · doi:10.1109/aisummit66170.2025.11410932

WHETSTONE: Honing Synthetic Data for Improved Downstream Fairness and Utility

Dhooghe, M. G. K., Zhou, Y., & Kantarcıoğlu, M. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00279

Advancing Ethical AI : Fairness, Diversity, and Privacy in Generative Modeling

Zameshina, M. (2024) · doi:10.70675/a42bd50cz2219z4ae7z8e39zb97fff084971

Reducing Disparity in Aleatoric Uncertainty for Age Estimation from Facial Images

Rahman, R., Fu, Z., Xu, D., & Niu, X. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-1947-6_14

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