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

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

Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information

Dai, E., & Wang, S. (2021) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3437963.3441752 · Cited by 273

Robustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applications

Javed, H., El–Sappagh, S., & Abuhmed, T. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-11005-9 · Cited by 228

Algorithmic Fairness

Pessach, D., & Shmueli, E. (2023) · Springer eBooks · doi:10.1007/978-3-031-24628-9_37 · Cited by 235

Fairness in Machine Learning: A Survey

Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646

StyleGAN-Human: A Data-Centric Odyssey of Human Generation

Fu, J., Li, S., Jiang, Y., Lin, K.-Y., Qian, C., Loy, C. C., Wu, W., & Liu, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19787-1_1 · Cited by 146

Fair Attribute Classification through Latent Space De-biasing

Ramaswamy, V., Kim, S. S. Y., & Russakovsky, O. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00918 · Cited by 122

What We Can't Measure, We Can't Understand: Challenges to Demographic Data Procurement in the Pursuit of Fairness

Andrus, M., Spitzer, E., Brown, J., & Xiang, A. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445888 · Cited by 111

Data Augmentation for Discrimination Prevention and Bias Disambiguation

Sharma, S., Zhang, Y., Ríos, J., Bouneffouf, D., Muthusamy, V., & Varshney, K. R. (2020) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3375627.3375865 · Cited by 94

The Problem of Fairness in Synthetic Healthcare Data

Bhanot, K., Qi, M., Erickson, J. S., Guyon, I., & Bennett, K. P. (2021) · Entropy · doi:10.3390/e23091165 · Cited by 94

Understanding and Evaluating Racial Biases in Image Captioning

Zhao, D., Wang, A., & Russakovsky, O. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01456 · Cited by 85

REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets

Wang, A., Liu, A., Zhang, R., Kleiman, A., Kim, L., Zhao, D., Shirai, I., Narayanan, A., & Russakovsky, O. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01625-5 · Cited by 95

TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks

Rajabi, A., & Garibay, Ö. Ö. (2022) · Machine Learning and Knowledge Extraction · doi:10.3390/make4020022 · Cited by 85

Meta Balanced Network for Fair Face Recognition

Wang, M., Zhang, Y., & Deng, W. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3103191 · Cited by 71

Bias in Multimodal AI: Testbed for Fair Automatic Recruitment

Peña, A., Serna, I., Morales, A., & Fiérrez, J. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00022 · Cited by 68

Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-Based Recruitment

Peña, A., Serna, I., Morales, A., Fiérrez, J., Ortega, A., Sánchez, A. H., Saéz, M. A., & Ortega-García, J. (2023) · SN Computer Science · doi:10.1007/s42979-023-01733-0 · Cited by 72

Cascaded frameworks in underwater optical image restoration

Li, B., Chen, Z., Lu, L., Qi, P., Zhang, L., Ma, Q., Hu, H., Zhai, J., & Li, X. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102809 · Cited by 57

Fair Generative Modeling via Weak SupervisionPreprint

Choi, K., Grover, A., Singh, T., Shu, R., & Ermon, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12008 · Cited by 58

Facial Beauty Prediction and Analysis based on Deep Convolutional Neural Network: A Review

Saeed, J. N., & Abdulazeez, A. M. (2021) · Journal of Soft Computing and Data Mining · doi:10.30880/jscdm.2021.02.01.001 · Cited by 53

Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals

Dash, S., Balasubramanian, V. N., & Sharma, A. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00393 · Cited by 59

Towards Fair Classifiers Without Sensitive Attributes

Zhao, T., Dai, E., Shu, K., & Wang, S. (2022) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3488560.3498493 · Cited by 47

FairNeuron

Gao, X., Zhai, J., Ma, S., Shen, C. Q., Chen, Y., & Wang, Q. (2022) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3510003.3510087 · Cited by 53

Learning Disentangled Representation for Fair Facial Attribute Classification via Fairness-aware Information Alignment

Park, S., Hwang, S., Kim, D., & Byun, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i3.16341 · Cited by 46

Inclusive GAN: Improving Data and Minority Coverage in Generative Models

Yu, N., Li, K., Zhou, P., Malik, J., Davis, L., & Fritz, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58542-6_23 · Cited by 48

Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers

Zietlow, D., Lohaus, M., Balakrishnan, G., Kleindeßner, M., Locatello, F., Schölkopf, B., & Russell, C. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01016 · Cited by 54

Data preprocessing to mitigate bias: A maximum entropy based approachPreprint

Celis, L. E., Keswani, V., & Vishnoi, N. K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02164 · Cited by 42

Learning Fair Graph Neural Networks with Limited and Private Sensitive Attribute Information

Dai, E., & Wang, S. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3197554 · Cited by 40

How to Promote AI in the US Federal Government: Insights from Policy Process Frameworks

Khan, M. S., Shoaib, A., & Arledge, E. (2023) · Government Information Quarterly · doi:10.1016/j.giq.2023.101908 · Cited by 41

Color-Theoretic Experiments to Understand Unequal Gender Classification Accuracy From Face Images

Muthukumar, V., Pedapati, T., Ratha, N., Sattigeri, P., Wu, C.-W., Kingsbury, B. E. D., Kumar, A., Thomas, S., Mojsilović, A., & Varshney, K. R. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00282 · Cited by 39

Improving the Fairness of Deep Generative Models without RetrainingPreprint

Tan, S., Shen, Y., & Zhou, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.04842 · Cited by 39

Fair Enough: Searching for Sufficient Measures of Fairness

Majumder, S., Chakraborty, J., Bai, G. R., Stolee, K. T., & Menzies, T. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3585006 · Cited by 36

Ethical Adversaries

Delobelle, P., Temple, P., Perrouin, G., Frénay‬, B., Heymans, P., & Berendt, B. (2021) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3468507.3468513 · Cited by 39

PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition

Dhar, P., Gleason, J., Roy, A., Castillo, C. D., & Chellappa, R. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01481 · Cited by 35

Constructing a Fair Classifier with Generated Fair Data

Jang, T., Zheng, F., & Wang, X. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i9.16965 · Cited by 36

Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction

Rehman, S. U., Riaz, R. D., Usman, M., & Kim, I. (2024) · Applied Sciences · doi:10.3390/app14167231 · Cited by 28

Analyzing Demographic Bias in Artificially Generated Facial Pictures

Salminen, J. O., Jung, S., Chowdhury, S. A., & Jansen, B. J. J. (2020) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3334480.3382791 · Cited by 27

Balancing Act: Distribution-Guided Debiasing in Diffusion Models

Parihar, R., Bhat, A., Basu, A., Mallick, S., Kundu, J. N., & Babu, R. V. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00637 · Cited by 36

Gender Artifacts in Visual Datasets

Meister, N., Zhao, D., Wang, A., Ramaswamy, V., Fong, R., & Russakovsky, O. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00446 · Cited by 38

Survey of Federated Learning Models for Spatial-Temporal Mobility Applications

Belal, Y., Mokhtar, S. B., Haddadi, H., Wang, J., & Mashhadi, A. (2024) · ACM Transactions on Spatial Algorithms and Systems · doi:10.1145/3666089 · Cited by 32

FairFaceGAN: Fairness-aware Facial Image-to-Image Translation

Hwang, S., Park, S., Kim, D., Do, M., & Byun, H. (2020) · British Machine Vision Conference (BMVC) · doi:10.5244/c.34.54 · Cited by 26

Rethinking Appearance-Based Deep Gait Recognition: Reviews, Analysis, and Insights From Gait Recognition Evolution

Li, J., Zhang, Y., Zeng, Y., Ye, C., Xu, W. Z., Ben, X., Wang, F., & Zhang, J. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3526815 · Cited by 24

SocialCounterfactuals: Probing and Mitigating Intersectional Social Biases in Vision-Language Models with Counterfactual Examples

Howard, P., Madasu, A., Le, T., Lujan-Moreno, G. A., Bhiwandiwalla, A., & Lal, V. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01138 · Cited by 37

End-To-End Bias Mitigation: Removing Gender Bias in Deep Learning

Feldman, T., & Peake, A. (2021) · arXiv · doi:10.48550/arxiv.2104.02532 · Cited by 19

Fair Generative Models via Transfer Learning

Teo, C. T. H., Abdollahzadeh, M., & Cheung, N. M. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i2.25339 · Cited by 19

Bias and Fairness in Computer Vision Applications of the Criminal Justice System

Noiret, S., Lumetzberger, J., & Kampel, M. (2021) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci50451.2021.9660177 · Cited by 21

Fair Federated Learning with Opposite GAN

Han, M., Zhu, T., & Zhou, W. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111420 · Cited by 17

Fair-VPT: Fair Visual Prompt Tuning for Image Classification

Park, S., & Byun, H. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01166 · Cited by 18

AI image generation technology in ophthalmology: Use, misuse and future applications

Phipps, B., Hadoux, X., Sheng, B., Campbell, J. P., Liu, T. Y. A., Keane, P. A., Cheung, C. Y.-L., Chung, T. Y., Wong, T. Y., & van Wijngaarden, P. (2025) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2025.101353 · Cited by 18

Fair Federated Learning for Heterogeneous Data

Kanaparthy, S., Padala, M., Damle, S., & Gujar, S. (2022) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3493700.3493750 · Cited by 16

Toward robust and privacy-enhanced facial recognition: A decentralized blockchain-based approach with GANs and deep learning

Ghani, M. A. N. U., Kun, S., Rauf, M. A., Khan, S., Alajmi, M., Ghadi, Y. Y., & Alkahtani, H. K. (2024) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2024184 · Cited by 15

Identifying and Mitigating Potential Biases in Predicting Drug Approvals

Xu, Q., Ahmadi, E., Amini, A., Rus, D. L., & Lo, A. W. (2022) · Drug Safety · doi:10.1007/s40264-022-01160-9 · Cited by 14

Addressing Fairness in Classification with a Model-Agnostic Multi-Objective AlgorithmPreprint

Padh, K. S., Antognini, D., Glaude, E. L., Faltings, B. V., & Musat, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.04441 · Cited by 14

Reducing Bias in Sentiment Analysis Models Through Causal Mediation Analysis and Targeted Counterfactual Training

Da, Y., Bossa, M. N., Berenguer, A. D., & Hichem Sahli, B. (2024) · IEEE Access · doi:10.1109/access.2024.3353056 · Cited by 16

Bias and Discrimination in Machine Decision-Making Systems

Casillas, J. (2023) · The International library of ethics, law and technology · doi:10.1007/978-3-031-48135-2_2 · Cited by 12

Unsupervised Ethical Equity Evaluation of Adversarial Federated Networks

Siniosoglou, I., Argyriou, V., Bibi, S., Λάγκας, Θ., & Sarigiannidis, P. G. (2021) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3465481.3470478 · Cited by 10

Classification Utility, Fairness, and Compactness via Tunable Information Bottleneck and Rényi Measures

Gronowski, A. A., Paul, W., Alajaji, F., Gharesifard, B., & Burlina, P. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3340094 · Cited by 10

Generative Models for Security: Attacks, Defenses, and OpportunitiesPreprint

Bauer, L. A., & Bindschaedler, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.10139 · Cited by 14

Latent Space Smoothing for Individually Fair Representations

Peychev, M., Ruoss, A., Balunović, M., Baader, M., & Vechev, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19778-9_31 · Cited by 11

Fair Sampling in Diffusion Models through Switching Mechanism

Choi, Y., Park, J.-G., Kim, H., Lee, J., & Park, S. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i20.30202 · Cited by 14

Environmental Adaptation and Differential Replication in Machine Learning

Unceta, I., Nin, J., & Pujol, O. (2020) · Entropy · doi:10.3390/e22101122 · Cited by 9

Forecasting day-ahead spot electricity prices using deep neural networks with attention mechanism

Marszałek, A., & Burczyński, T. (2021) · Journal of Smart Environments and Green Computing · doi:10.20517/jsegc.2021.02 · Cited by 9

FairGNN: Eliminating the Discrimination in Graph Neural Networks with Limited Sensitive Attribute Information.Preprint

Dai, E., & Wang, S. (2020) · arXiv (Cornell University) · Cited by 9

You Can Still Achieve Fairness Without Sensitive Attributes: Exploring Biases in Non-Sensitive Features.Preprint

Zhao, T., Dai, E., Shu, K., & Wang, S. (2021) · arXiv (Cornell University) · Cited by 9

Hardware design and the fairness of a neural network

Guo, Y., Yan, Z., Yu, X., Kong, Q., Xie, J., Luo, K., Zeng, D., Wu, Y., Jia, Z., & Shi, Y. (2024) · Nature Electronics · doi:10.1038/s41928-024-01213-0 · Cited by 8

Content preserving image translation with texture co-occurrence and spatial self-similarity for texture debiasing and domain adaptation

Kang, M., Won, D., Luna, M., Chikontwe, P., Hong, K. S., Ahn, J. H., & Park, S. H. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.07.049 · Cited by 8

G-Tric: generating three-way synthetic datasets with triclustering solutions

Lobo, J., Henriques, R., & Madeira, S. C. (2021) · BMC Bioinformatics · doi:10.1186/s12859-020-03925-4 · Cited by 8

GenEthos: A Synthetic Data Generation System With Bias Detection And Mitigation

Gujar, S. A., Shah, T., Honawale, D., Bhosale, V., Khan, F., Verma, D., & Ranjan, R. (2022) · International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS) · doi:10.1109/ic3sis54991.2022.9885653 · Cited by 9

Exploiting Transferable Knowledge for Fairness-Aware Image Classification

Hwang, S., Park, S., Lee, P., Jeon, S., Kim, D., & Byun, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69538-5_2 · Cited by 8

Mitigating health disparities in EHR via deconfounder

Liu, Z., Li, X., & Yu, P. S. (2022) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3535508.3545516 · Cited by 8

On Mismatched Detection and Safe, Trustworthy Machine Learning

Varshney, K. R. (2020) · Conference on Information Sciences and Systems · doi:10.1109/ciss48834.2020.1570627767 · Cited by 7

Information-Minimizing Generative Adversarial Network for Fair Generation and Classification

Chen, Q., Ye, A., Zhang, Y., Chen, J., & Huang, C. (2024) · Neural Processing Letters · doi:10.1007/s11063-024-11457-8 · Cited by 7

Self-Conditioned GANs for Image Editing

Liu, Y., Gal, R., Bermano, A. H., Chen, B., & Cohen‐Or, D. (2022) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/3528233.3530698 · Cited by 7

Through a Fair Looking-Glass: Mitigating Bias in Image Datasets

Rajabi, A., Yazdani-Jahromi, M., Garibay, Ö. Ö., & Sukthankar, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35891-3_27 · Cited by 9

FairTL: A Transfer Learning Approach for Bias Mitigation in Deep Generative Models

Teo, C. T. H., Abdollahzadeh, M., & Cheung, N. M. (2024) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2024.3363419 · Cited by 6

E-VAN : Enhanced Variational AutoEncoder Network for Mitigating Gender Bias in Static Word Embeddings

Tyagi, S., Xie, J., & Andrews, R. (2022) · International Conference on Natural Language Processing and Information Retrieval (NLPIR) · doi:10.1145/3582768.3582804 · Cited by 6

Addressing diversity in hiring procedures: a generative adversarial network approach

Marra, T., & Kubiak, E. (2024) · AI and Ethics · doi:10.1007/s43681-024-00445-2 · Cited by 6

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

The double-edged sword: A critical review of foundational medical datasets for AI benchmarks, biases, and the future of equitable healthcare

Ramadan, R. Α., Madi, N. K. M., Fageeri, S. O., Yousif, K. A., Al-Daweri, M. S., & Rajhi, W. K. H. A. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.132919 · Cited by 5

Fairness Meets Cross-Domain Learning: A Benchmark of Models and Metrics

Iurada, L., Bucci, S., Hospedales, T. M., & Tommasi, T. (2024) · IEEE Access · doi:10.1109/access.2024.3383841 · Cited by 5

A Generative Approach to Mitigate Bias in Face Matching using Learned Latent Structure

Alasadi, J., AlHilli, A., Atrey, P. K., & Singh, V. K. (2022) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm55396.2022.00033 · Cited by 5

Fairness in generative modeling

Zameshina, M., Teytaud, O., Teytaud, F., Hosu, V., Rakotonirina, N. C., Najman, L. A., & Wagner, M. (2022) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3520304.3528992 · Cited by 5

Mitigating bias in facial analysis systems by incorporating label diversity

Kolling, C., Araujo, V., Veloso, A., & Musse, S. R. (2023) · Computers & Graphics · doi:10.1016/j.cag.2023.08.021 · Cited by 5

Adversarial Fairness Network

Jang, T., Wang, X., & Huang, H. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i20.30220 · Cited by 4

Fairness in machine learning: definition, testing, debugging, and application

Gao, X., Shen, C. Q., Jiang, W., Lin, C., Li, Q., Wang, Q., Li, Q., & Guan, X. (2024) · Science China Information Sciences · doi:10.1007/s11432-023-4060-x · Cited by 4

Thinking in Categories: A Survey on Assessing the Quality for Time Series Synthesis

Stenger, M., Bauer, A., Prantl, T., Leppich, R., Hudson, N., Chard, K., Foster, I. T., & Kounev, S. (2024) · Journal of Data and Information Quality · doi:10.1145/3666006 · Cited by 6

Benchmarking the Fairness of Image Upsampling Methods

Laszkiewicz, M., Daunhawer, I., Vogt, J. E., Fischer, A., & Lederer, J. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658921 · Cited by 7

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

CAT: Controllable Attribute Translation for Fair Facial Attribute Classification

Li, J., & AbdAlmageed, W. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25085-9_21 · Cited by 4

A Fair Generative Model Using LeCam Divergence

Um, S., & Suh, C. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i8.26196 · Cited by 3

A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfounder

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