On Measuring Fairness in Generative Models
Christopher T. H. Teo Milad Abdollahzadeh Ngai-Man Cheung
Recently, there has been increased interest in fair generative models. In
this work, we conduct, for the first time, an in-depth study on fairness
measurement, a critical component in gauging progress on fair generative
models. We make three contributions. First, we conduct a study that reveals
that the existing fairness measurement framework has considerable measurement
errors, even when highly accurate sensitive attribute (SA) classifiers are
used. These findings cast doubts on previously reported fairness improvements.
Second, to address this issue, we propose CLassifier Error-Aware Measurement
(CLEAM), a new framework which uses a statistical model to account for
inaccuracies in SA classifiers. Our proposed CLEAM reduces measurement errors
significantly, e.g., 4.98% $\rightarrow$ 0.62% for StyleGAN2 w.r.t. Gender.
Additionally, CLEAM achieves this with minimal additional overhead. Third, we
utilize CLEAM to measure fairness in important text-to-image generator and
GANs, revealing considerable biases in these models that raise concerns about
their applications. Code and more resources:
https://sutd-visual-computing-group.github.io/CLEAM/.