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 · see citing works

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 · see citing works

Eigenfaces for Recognition

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

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 · see citing works

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 · see citing works

Categorical Reparameterization with Gumbel-Softmax

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

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 · see citing works

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 2,968 · see citing works

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 1,771 · see citing works

NIPS 2016 Tutorial: Generative Adversarial NetworksPreprint

Goodfellow, I. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.00160 · Cited by 1,735 · see citing works

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 · see citing works

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 · see citing works

A Learned Representation For Artistic StylePreprint

Dumoulin, V., Shlens, J., & Kudlur, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.07629 · Cited by 1,033 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Censoring Representations with an AdversaryPreprint

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

cGANs with Projection DiscriminatorPreprint

Miyato, T., & Koyama, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.05637 · Cited by 464 · see citing works

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 418 · see citing works

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 · see citing works

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 · see citing works

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 261 · see citing works

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 · see citing works

Obstructing Classification via Projection

Haghighatkhah, P., Meulemans, W., Speckmann, B., et al. (2021) · Leibniz international proceedings in informatics · doi:10.4230/lipics.mfcs.2021.56 · Cited by 229 · see citing works

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 · see citing works

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 203 · see citing works

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 · see citing works

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 · see citing works

When “the state of the art” is counting words

Perelman, L. (2014) · Assessing Writing · doi:10.1016/j.asw.2014.05.001 · Cited by 129 · see citing works

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 · see citing works

Conditional CycleGAN for Attribute Guided Face Image GenerationPreprint

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

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 · see citing works

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 · see citing works

Fairness in machine learning: A survey

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

A Review on Fairness in Machine Learning

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

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 349 · see citing works

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 · see citing works

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 261 · see citing works

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 205 · see citing works

Algorithmic Fairness

Pessach, D., & Shmueli, E. (2023) · Springer eBooks · doi:10.1007/978-3-031-24628-9_37 · Cited by 177 · see citing works

Fairness in Machine Learning: A Survey

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

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 127 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 84 · see citing works

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 85 · see citing works

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 75 · see citing works

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 69 · see citing works

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 · see citing works

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 62 · see citing works

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 · see citing works

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 54 · see citing works

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 · see citing works

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 50 · see citing works

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 · see citing works

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 46 · see citing works

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 · see citing works

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 45 · see citing works

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 45 · see citing works

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 · see citing works

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 · see citing works

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 39 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 35 · see citing works

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 · see citing works

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 31 · see citing works

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 · see citing works

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 · see citing works

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 26 · see citing works

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 26 · see citing works

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 26 · see citing works

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 · see citing works

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 · see citing works

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 22 · see citing works

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 · see citing works

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 · see citing works

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 18 · see citing works

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 · see citing works

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 17 · see citing works

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 16 · see citing works

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 15 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 12 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 10 · see citing works

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 9 · see citing works

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 9 · see citing works

Environmental Adaptation and Differential Replication in Machine Learning

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

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 8 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 7 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 6 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 4 · see citing works

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 4 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfounder

Liu, Z., Li, X., & Yu, P. S. (2023) · Proceedings · doi:10.1109/icdm58522.2023.00144 · Cited by 3 · see citing works

Muffin: A Framework Toward Multi-Dimension AI Fairness by Uniting Off-the-Shelf Models

Sheng, Y., Yang, J., Yang, L., Shi, Y., Hu, J., & Jiang, W. (2023) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac56929.2023.10247765 · Cited by 3 · see citing works

Adversarial Learning-based Bias Mitigation for Fatigue Driving Detection in Fair-Intelligent IoV

Han, M., Wu, J., Bashir, A. K., Yang, W., Imran, M. A., & Nasser, N. (2020) · Globecom · doi:10.1109/globecom42002.2020.9322194 · Cited by 3 · see citing works

Towards fair machine learning using combinatorial methods

Saraswat, A., Pal, M., Pokhriyal, S., & Abhishek, K. (2022) · Evolutionary Intelligence · doi:10.1007/s12065-022-00702-5 · Cited by 3 · see citing works

Bias Mitigation through Proxy Sensitive Attribute Label Generation

Tiwari, D., Pandey, A., & Chaudhari, B. P. (2024) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3703323.3703329 · Cited by 2 · see citing works

Studying bias in visual features through the lens of optimal transport

Fabbrizzi, S., Zhao, X., Krasanakis, E., Papadopoulos, S., & Ntoutsi, E. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00972-2 · Cited by 2 · see citing works

Enhancing machine learning efficacy and fairness in automated decision systems: an adversarial deep generative modeling with CoBS-TGAN approach in imbalanced and biased datasets

Bhat, K. A., & Sofi, S. A. (2024) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-024-02430-8 · Cited by 2 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 6 · see citing works