Synthetic Tabular Data for Model Development: Utility, Disclosure Risk and Regulatory Acceptance
DOI:
https://doi.org/10.5281/zenodo.22660803Keywords:
Synthetic Tabular Data for Model Development; Applied machine learning; Evidence synthesis; Production deployment; MLOpsAbstract
Synthetic Tabular Data for Model Development is an area of active development in applied machine learning, with evidence that
has accumulated unevenly across its component questions. We review the current evidence base for synthetic tabular data for
model development, spanning methodological, implementation, governance, and cost dimensions, focusing on where practice has
converged and where it continues to vary, and set out the practical considerations relevant to a team considering its adoption. We
conclude that selective, context-specific use is best supported by the evidence currently available, rather than uniform adoption or
avoidance.
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