# Representative & Fair Synthetic Data

arXiv: https://arxiv.org/abs/2104.03007 · HTML: https://mlsanity.com/paper/2104.03007/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 7 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [Fairness in Machine Learning](https://mlsanity.com/paper/2012.15816/) (2020)
- [Inherent Tradeoffs in Learning Fair Representations](https://mlsanity.com/paper/1906.08386/) (2019)
- [FNNC: Achieving Fairness through Neural Networks](https://mlsanity.com/paper/1811.00247/) (2018)
- [A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set](https://mlsanity.com/paper/2003.14263/) (2020)

## Built on by (most cited first, 8 of 8)

- [Synthetic Data -- what, why and how?](https://mlsanity.com/paper/2205.03257/) (2022)
- [On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Models](https://mlsanity.com/paper/2108.00114/) (2021)
- [Strong statistical parity through fair synthetic data](https://mlsanity.com/paper/2311.03000/) (2023)
- [ChildGAN: Large Scale Synthetic Child Facial Data Using Domain Adaptation in StyleGAN](https://mlsanity.com/paper/2307.13746/) (2023)
- [Rule-adhering synthetic data -- the lingua franca of learning](https://mlsanity.com/paper/2209.06679/) (2022)
- [Downstream Fairness Caveats with Synthetic Healthcare Data](https://mlsanity.com/paper/2203.04462/) (2022)
- [Fair Machine Learning in Healthcare: A Review](https://mlsanity.com/paper/2206.14397/) (2022)
- [(Machine) Learning to Be Like Thee? For Algorithm Education, Not Training](https://mlsanity.com/paper/2305.12157/) (2023)

## Abstract (arXiv)

Algorithms learn rules and associations based on the training data that they are exposed to. Yet, the very same data that teaches machines to understand and predict the world, contains societal and historic biases, resulting in biased algorithms with the risk of further amplifying these once put into use for decision support. Synthetic data, on the other hand, emerges with the promise to provide an unlimited amount of representative, realistic training samples, that can be shared further without disclosing the privacy of individual subjects. We present a framework to incorporate fairness constraints into the self-supervised learning process, that allows to then simulate an unlimited amount of representative as well as fair synthetic data. This framework provides a handle to govern and control for privacy as well as for bias within AI at its very source: the training data. We demonstrate the proposed approach by amending an existing generative model architecture and generating a representative as well as fair version of the UCI Adult census data set. While the relationships between attributes are faithfully retained, the gender and racial biases inherent in the original data are controlled for. This is further validated by comparing propensity scores of downstream predictive models that are trained on the original data versus the fair synthetic data. We consider representative & fair synthetic data a promising future building block to teach algorithms not on historic worlds, but rather on the worlds that we strive to live in.