Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic
Data
- URL: http://arxiv.org/abs/2304.03722v1
- Date: Fri, 7 Apr 2023 16:38:40 GMT
- Title: Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic
Data
- Authors: Boris van Breugel and Mihaela van der Schaar
- Abstract summary: Synthetic data may become a dominant force in the machine learning world, promising a future where datasets can be tailored to individual needs.
We discuss which fundamental challenges the community needs to overcome for wider relevance and application of synthetic data.
- Score: 91.52783572568214
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Generating synthetic data through generative models is gaining interest in
the ML community and beyond. In the past, synthetic data was often regarded as
a means to private data release, but a surge of recent papers explore how its
potential reaches much further than this -- from creating more fair data to
data augmentation, and from simulation to text generated by ChatGPT. In this
perspective we explore whether, and how, synthetic data may become a dominant
force in the machine learning world, promising a future where datasets can be
tailored to individual needs. Just as importantly, we discuss which fundamental
challenges the community needs to overcome for wider relevance and application
of synthetic data -- the most important of which is quantifying how much we can
trust any finding or prediction drawn from synthetic data.
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