# TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2109.00666 · HTML: https://mlsanity.com/paper/2109.00666/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 18 of 18)

- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Generating Videos with Scene Dynamics](https://mlsanity.com/paper/1609.02612/) (2016)
- [Learning Adversarially Fair and Transferable Representations](https://mlsanity.com/paper/1802.06309/) (2018)
- [Generating Multi-label Discrete Patient Records using Generative Adversarial Networks](https://mlsanity.com/paper/1703.06490/) (2017)
- [Censoring Representations with an Adversary](https://mlsanity.com/paper/1511.05897/) (2015)
- [Data Synthesis based on Generative Adversarial Networks](https://mlsanity.com/paper/1806.03384/) (2018)
- [FairGAN: Fairness-aware Generative Adversarial Networks](https://mlsanity.com/paper/1805.11202/) (2018)
- [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020)
- [Synthesizing Tabular Data using Generative Adversarial Networks](https://mlsanity.com/paper/1811.11264/) (2018)
- [Fairness GAN](https://mlsanity.com/paper/1805.09910/) (2018)

## Built on by (most cited first, 30 of 34)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [A Comprehensive Survey of Synthetic Tabular Data Generation](https://mlsanity.com/paper/2504.16506/) (2025)
- [TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Data](https://mlsanity.com/paper/2207.05295/) (2022)
- [D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias](https://mlsanity.com/paper/2208.05126/) (2022)
- [SurvivalGAN: Generating Time-to-Event Data for Survival Analysis](https://mlsanity.com/paper/2302.12749/) (2023)
- [Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models](https://mlsanity.com/paper/2404.08254/) (2024)
- [Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium](https://mlsanity.com/paper/2410.16432/) (2024)
- [Generating Realistic Tabular Data with Large Language Models](https://mlsanity.com/paper/2410.21717/) (2024)
- [Through a fair looking-glass: mitigating bias in image datasets](https://mlsanity.com/paper/2209.08648/) (2022)
- [Strong statistical parity through fair synthetic data](https://mlsanity.com/paper/2311.03000/) (2023)
- [The Real Deal Behind the Artificial Appeal: Inferential Utility of Tabular Synthetic Data](https://mlsanity.com/paper/2312.07837/) (2023)
- [Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study](https://mlsanity.com/paper/2409.05215/) (2024)
- [Consistent End-to-End Estimation for Counterfactual Fairness](https://mlsanity.com/paper/2310.17687/) (2023)
- [FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN](https://mlsanity.com/paper/2403.16930/) (2024)
- [Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithms](https://mlsanity.com/paper/2501.01785/) (2025)
- [FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation](https://mlsanity.com/paper/2506.19082/) (2025)
- [Harnessing Synthetic Data from Generative AI for Statistical Inference](https://mlsanity.com/paper/2603.05396/) (2026)
- [Distraction is All You Need for Fairness](https://mlsanity.com/paper/2203.07593/) (2022)
- [FairRR: Pre-Processing for Group Fairness through Randomized Response](https://mlsanity.com/paper/2403.07780/) (2024)
- [FairX: A comprehensive benchmarking tool for model analysis using fairness, utility, and explainability](https://mlsanity.com/paper/2406.14281/) (2024)
- [TABFAIRGDT: A Fast Fair Tabular Data Generator using Autoregressive Decision Trees](https://mlsanity.com/paper/2509.19927/) (2025)
- [Increasing Fairness in Predictions Using Bias Parity Score Based Loss Function Regularization](https://mlsanity.com/paper/2111.03638/) (2021)
- [BSGAN: A Novel Oversampling Technique for Imbalanced Pattern Recognitions](https://mlsanity.com/paper/2305.09777/) (2023)
- [TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start](https://mlsanity.com/paper/2311.18749/) (2023)
- [Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios](https://mlsanity.com/paper/2407.03080/) (2024)
- [Privacy-Preserving Fair Synthetic Tabular Data](https://mlsanity.com/paper/2503.02968/) (2025)
- [Causality Learning With Wasserstein Generative Adversarial Networks](https://mlsanity.com/paper/2206.01496/) (2022)
- [DSF-GAN: DownStream Feedback Generative Adversarial Network](https://mlsanity.com/paper/2403.18267/) (2024)
- [Fair4Free: Generating High-fidelity Fair Synthetic Samples using Data Free Distillation](https://mlsanity.com/paper/2410.01423/) (2024)
- [Synthetic Data: AI's New Weapon Against Android Malware](https://mlsanity.com/paper/2511.19649/) (2025)

## Abstract (arXiv)

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. 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 state-of-the-art GANs proposed in the literature to produce synthetic tabular data. Also, 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, by implementing a Wasserstein GAN.