  Measuring biases of vision systems with respect to protected attributes like
gender and age is critical as these systems gain widespread use in society.
However, significant correlations between attributes in benchmark datasets make
it difficult to separate algorithmic bias from dataset bias. To mitigate such
attribute confounding during bias analysis, we propose a matching approach that
selects a subset of images from the full dataset with balanced attribute
distributions across protected attributes. Our matching approach first projects
real images onto a generative adversarial network (GAN)'s latent space in a
manner that preserves semantic attributes. It then finds image matches in this
latent space across a chosen protected attribute, yielding a dataset where
semantic and perceptual attributes are balanced across the protected attribute.
We validate projection and matching strategies with qualitative, quantitative,
and human annotation experiments. We demonstrate our work in the context of
gender bias in multiple open-source facial-recognition classifiers and find
that bias persists after removing key confounders via matching. Code and
documentation to reproduce the results here and apply the methods to new data
is available at https://github.com/csinva/matching-with-gans .
