# Fairness in generative modeling

arXiv: https://arxiv.org/abs/2210.03517 · HTML: https://mlsanity.com/paper/2210.03517/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 32 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 19 of 19)

- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models](https://mlsanity.com/paper/2003.03808/) (2020)
- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [FairGAN: Fairness-aware Generative Adversarial Networks](https://mlsanity.com/paper/1805.11202/) (2018)
- [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020)
- [Fair Generative Modeling via Weak Supervision](https://mlsanity.com/paper/1910.12008/) (2019)
- [On GANs and GMMs](https://mlsanity.com/paper/1805.12462/) (2018)
- [Effective Aesthetics Prediction with Multi-level Spatially Pooled Features](https://mlsanity.com/paper/1904.01382/) (2019)
- [Improving the Fairness of Deep Generative Models without Retraining](https://mlsanity.com/paper/2012.04842/) (2020)
- [Fairness GAN](https://mlsanity.com/paper/1805.09910/) (2018)
- [Fairness for Image Generation with Uncertain Sensitive Attributes](https://mlsanity.com/paper/2106.12182/) (2021)
- [Measuring Fairness in Generative Models](https://mlsanity.com/paper/2107.07754/) (2021)
- [FairFaceGAN: Fairness-aware Facial Image-to-Image Translation](https://mlsanity.com/paper/2012.00282/) (2020)
- [On the Fairness of Generative Adversarial Networks (GANs)](https://mlsanity.com/paper/2103.00950/) (2021)
- [EvolGAN: Evolutionary Generative Adversarial Networks](https://mlsanity.com/paper/2009.13311/) (2020)
- [Self-adaptation of Mutation Rates in Non-elitist Populations](https://mlsanity.com/paper/1606.05551/) (2016)
- [Inspirational Adversarial Image Generation](https://mlsanity.com/paper/1906.11661/) (2019)
- [Fairness in Online Social Network Timelines: Measurements, Models and Mechanism Design](https://mlsanity.com/paper/1809.05530/) (2018)

## Built on by (most cited first, 2 of 2)

- [Benchmarking the Fairness of Image Upsampling Methods](https://mlsanity.com/paper/2401.13555/) (2024)
- [Evolutionary Retrofitting](https://mlsanity.com/paper/2410.11330/) (2024)

## Abstract (arXiv)

We design general-purpose algorithms for addressing fairness issues and mode collapse in generative modeling. More precisely, to design fair algorithms for as many sensitive variables as possible, including variables we might not be aware of, we assume no prior knowledge of sensitive variables: our algorithms use unsupervised fairness only, meaning no information related to the sensitive variables is used for our fairness-improving methods. All images of faces (even generated ones) have been removed to mitigate legal risks.