  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.
