  Generative models can serve as surrogates for some real data sources by
creating synthetic training datasets, but in doing so they may transfer biases
to downstream tasks. We focus on protecting quality and diversity when
generating synthetic training datasets. We propose quality-diversity generative
sampling (QDGS), a framework for sampling data uniformly across a user-defined
measure space, despite the data coming from a biased generator. QDGS is a
model-agnostic framework that uses prompt guidance to optimize a quality
objective across measures of diversity for synthetically generated data,
without fine-tuning the generative model. Using balanced synthetic datasets
generated by QDGS, we first debias classifiers trained on color-biased shape
datasets as a proof-of-concept. By applying QDGS to facial data synthesis, we
prompt for desired semantic concepts, such as skin tone and age, to create an
intersectional dataset with a combined blend of visual features. Leveraging
this balanced data for training classifiers improves fairness while maintaining
accuracy on facial recognition benchmarks. Code available at:
https://github.com/Cylumn/qd-generative-sampling.
