# The Use of Synthetic Data to Train AI Models: Opportunities and Risks for Sustainable Development

arXiv: https://arxiv.org/abs/2309.00652 · HTML: https://mlsanity.com/paper/2309.00652/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 2 of 2)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs](https://mlsanity.com/paper/2010.03975/) (2020)

## Built on by (most cited first, 6 of 6)

- [The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Text](https://mlsanity.com/paper/2311.09807/) (2023)
- [An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Face](https://mlsanity.com/paper/2502.04484/) (2025)
- [Modelling and Classifying the Components of a Literature Review](https://mlsanity.com/paper/2508.04337/) (2025)
- [Exploring the Landscape for Generative Sequence Models for Specialized Data Synthesis](https://mlsanity.com/paper/2411.01929/) (2024)
- [Data Safety: Synthetic Data Quality Analysis Using CIFAKE Dataset](https://mlsanity.com/paper/2607.12165/) (2026)
- [Climate-ModernBERT: Revisiting Corpus Composition for Domain-Adaptive Continued Pretraining](https://mlsanity.com/paper/2609.07798/) (2026)

## Abstract (arXiv)

In the current data driven era, synthetic data, artificially generated data that resembles the characteristics of real world data without containing actual personal information, is gaining prominence. This is due to its potential to safeguard privacy, increase the availability of data for research, and reduce bias in machine learning models. This paper investigates the policies governing the creation, utilization, and dissemination of synthetic data. Synthetic data can be a powerful instrument for protecting the privacy of individuals, but it also presents challenges, such as ensuring its quality and authenticity. A well crafted synthetic data policy must strike a balance between privacy concerns and the utility of data, ensuring that it can be utilized effectively without compromising ethical or legal standards. Organizations and institutions must develop standardized guidelines and best practices in order to capitalize on the benefits of synthetic data while addressing its inherent challenges.