{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:54:34Z","timestamp":1787028874992,"version":"3.56.0"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T00:00:00Z","timestamp":1652659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>With the increasing reliance on automated decision making, the issue of algorithmic fairness has gained increasing importance. In this paper, we propose a Generative Adversarial Network for tabular data generation. The model includes two phases of training. In the first phase, the model is trained to accurately generate synthetic data similar to the reference dataset. In the second phase we modify the value function to add fairness constraint, and continue training the network to generate data that is both accurate and fair. We test our results in both cases of unconstrained, and constrained fair data generation. We show that using a fairly simple architecture and applying quantile transformation of numerical attributes the model achieves promising performance. In the unconstrained case, i.e., when the model is only trained in the first phase and is only meant to generate accurate data following the same joint probability distribution of the real data, the results show that the model beats the state-of-the-art GANs proposed in the literature to produce synthetic tabular data. Furthermore, in the constrained case in which the first phase of training is followed by the second phase, we train the network and test it on four datasets studied in the fairness literature and compare our results with another state-of-the-art pre-processing method, and present the promising results that it achieves. Comparing to other studies utilizing GANs for fair data generation, our model is comparably more stable by using only one critic, and also by avoiding major problems of original GAN model, such as mode-dropping and non-convergence.<\/jats:p>","DOI":"10.3390\/make4020022","type":"journal-article","created":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T21:36:06Z","timestamp":1652736966000},"page":"488-501","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8328-8473","authenticated-orcid":false,"given":"Amirarsalan","family":"Rajabi","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9215-694X","authenticated-orcid":false,"given":"Ozlem Ozmen","family":"Garibay","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA"},{"name":"Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1089\/big.2016.0047","article-title":"Fair prediction with disparate impact: A study of bias in recidivism prediction instruments","volume":"5","author":"Chouldechova","year":"2017","journal-title":"Big Data"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2966","DOI":"10.1287\/mnsc.2018.3093","article-title":"Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of stem career ads","volume":"65","author":"Lambrecht","year":"2019","journal-title":"Manag. Sci."},{"key":"ref_3","unstructured":"Pessach, D., and Shmueli, E. (2020). Algorithmic fairness. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-011-0463-8","article-title":"Data preprocessing techniques for classification without discrimination","volume":"33","author":"Kamiran","year":"2012","journal-title":"Knowl. Inf. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Feldman, M., Friedler, S.A., Moeller, J., Scheidegger, C., and Venkatasubramanian, S. (2015, January 10\u201313). Certifying and removing disparate impact. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, Australia.","DOI":"10.1145\/2783258.2783311"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kamishima, T., Akaho, S., Asoh, H., and Sakuma, J. (2012). Fairness-aware classifier with prejudice remover regularizer. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer.","DOI":"10.1007\/978-3-642-33486-3_3"},{"key":"ref_7","first-page":"3315","article-title":"Equality of opportunity in supervised learning","volume":"29","author":"Hardt","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Oussidi, A., and Elhassouny, A. (2018, January 2\u20134). Deep generative models: Survey. Proceedings of the 2018 International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, Morocco.","DOI":"10.1109\/ISACV.2018.8354080"},{"key":"ref_9","unstructured":"Fahlman, S.E., Hinton, G.E., and Sejnowski, T.J. (1983, January 22\u201326). Massively parallel architectures for Al: NETL, Thistle, and Boltzmann machines. Proceedings of the National Conference on Artificial Intelligence, AAAI, Washington, DC, USA."},{"key":"ref_10","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_11","unstructured":"Brock, A., Donahue, J., and Simonyan, K. (2018). Large scale GAN training for high fidelity natural image synthesis. arXiv."},{"key":"ref_12","first-page":"613","article-title":"Generating videos with scene dynamics","volume":"29","author":"Vondrick","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0016-0032(96)00063-4","article-title":"The jensen-shannon divergence","volume":"334","author":"Pardo","year":"1997","journal-title":"J. Frankl. Inst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1023\/A:1026543900054","article-title":"The earth mover\u2019s distance as a metric for image retrieval","volume":"40","author":"Rubner","year":"2000","journal-title":"Int. J. Comput. Vis."},{"key":"ref_15","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6\u201311). Wasserstein generative adversarial networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_16","unstructured":"Edwards, H., and Storkey, A. (2015). Censoring representations with an adversary. arXiv."},{"key":"ref_17","unstructured":"Madras, D., Creager, E., Pitassi, T., and Zemel, R. (2018, January 10\u201315). Learning adversarially fair and transferable representations. Proceedings of the International Conference on Machine Learning, Stockholm, Sweden."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, B.H., Lemoine, B., and Mitchell, M. (2018, January 2\u20133). Mitigating unwanted biases with adversarial learning. Proceedings of the 2018 AAAI\/ACM Conference on AI, Ethics, and Society, New Orleans, LA, USA.","DOI":"10.1145\/3278721.3278779"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3:1","DOI":"10.1147\/JRD.2019.2945519","article-title":"Fairness GAN: Generating datasets with fairness properties using a generative adversarial network","volume":"63","author":"Sattigeri","year":"2019","journal-title":"IBM J. Res. Dev."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xu, D., Yuan, S., Zhang, L., and Wu, X. (2018, January 10\u201313). Fairgan: Fairness-aware generative adversarial networks. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8622525"},{"key":"ref_21","unstructured":"Choi, E., Biswal, S., Malin, B., Duke, J., Stewart, W.F., and Sun, J. (2017, January 18\u201319). Generating multi-label discrete patient records using generative adversarial networks. Proceedings of the Machine Learning for Healthcare Conference, Boston, MA, USA."},{"key":"ref_22","unstructured":"Xu, L., and Veeramachaneni, K. (2018). Synthesizing Tabular Data using Generative Adversarial Networks. arXiv."},{"key":"ref_23","first-page":"7333","article-title":"Modeling Tabular data using Conditional GAN","volume":"32","author":"Xu","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xu, D., Yuan, S., Zhang, L., and Wu, X. (2019, January 9\u201312). Fairgan+: Achieving fair data generation and classification through generative adversarial nets. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9006322"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1007\/s10519-009-9281-0","article-title":"Rank-based inverse normal transformations are increasingly used, but are they merited?","volume":"39","author":"Beasley","year":"2009","journal-title":"Behav. Genet."},{"key":"ref_26","first-page":"5769","article-title":"Improved Training of Wasserstein GANs","volume":"30","author":"Gulrajani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Villani, C. (2009). Optimal Transport: Old and New, Springer.","DOI":"10.1007\/978-3-540-71050-9"},{"key":"ref_28","unstructured":"Jang, E., Gu, S., and Poole, B. (2016). Categorical reparameterization with gumbel-softmax. arXiv."},{"key":"ref_29","unstructured":"Xu, B., Wang, N., Chen, T., and Li, M. (2015). Empirical evaluation of rectified activations in convolutional network. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.dss.2014.03.001","article-title":"A data-driven approach to predict the success of bank telemarketing","volume":"62","author":"Moro","year":"2014","journal-title":"Decis. Support Syst."},{"key":"ref_31","unstructured":"Zafar, M.B., Valera, I., Rogriguez, M.G., and Gummadi, K.P. (2017, January 9\u201311). Fairness constraints: Mechanisms for fair classification. Proceedings of the Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Angwin, J., Larson, J., Mattu, S., and Kirchner, L. (2021, July 21). Machine Bias ProPublica. Available online: https:\/\/github.com\/propublica\/compas-analysis.","DOI":"10.1201\/9781003278290-37"},{"key":"ref_33","unstructured":"Wightman, L.F. (2021, July 20). LSAC National Longitudinal Bar Passage Study, Available online: https:\/\/eric.ed.gov\/?id=ED469370."},{"key":"ref_34","unstructured":"Bechavod, Y., and Ligett, K. (2017). Penalizing unfairness in binary classification. arXiv."},{"key":"ref_35","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/4\/2\/22\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:11:09Z","timestamp":1760137869000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/4\/2\/22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,16]]},"references-count":35,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["make4020022"],"URL":"https:\/\/doi.org\/10.3390\/make4020022","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,16]]}}}