# In Defense of Synthetic Data

arXiv: https://arxiv.org/abs/1905.01351 · HTML: https://mlsanity.com/paper/1905.01351/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2019.

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

- [DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series](https://mlsanity.com/paper/2203.15009/) (2022)
- [Sometimes the Model doth Preach: Quantifying Religious Bias in Open LLMs through Demographic Analysis in Asian Nations](https://mlsanity.com/paper/2503.07510/) (2025)

## Abstract (arXiv)

Synthetic datasets have long been thought of as second-rate, to be used only when "real" data collected directly from the real world is unavailable. But this perspective assumes that raw data is clean, unbiased, and trustworthy, which it rarely is. Moreover, the benefits of synthetic data for privacy and for bias correction are becoming increasingly important in any domain that works with people. Curated synthetic datasets - synthetic data derived from minimal perturbations of real data - enable early stage product development and collaboration, protect privacy, afford reproducibility, increase dataset diversity in research, and protect disadvantaged groups from problematic inferences on the original data that reflects systematic discrimination. Rather than representing a departure from the true state of the world, in this paper we argue that properly generated synthetic data is a step towards responsible and equitable research and development of machine learning systems.