Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss
Moritz Vandenhirtz Laura Manduchi Ričards Marcinkevičs Julia E. Vogt
Spurious correlations are everywhere. While humans often do not perceive
them, neural networks are notorious for learning unwanted associations, also
known as biases, instead of the underlying decision rule. As a result,
practitioners are often unaware of the biased decision-making of their
classifiers. Such a biased model based on spurious correlations might not
generalize to unobserved data, leading to unintended, adverse consequences. We
propose Signal is Harder (SiH), a variational-autoencoder-based method that
simultaneously trains a biased and unbiased classifier using a novel,
disentangling reweighting scheme inspired by the focal loss. Using the unbiased
classifier, SiH matches or improves upon the performance of state-of-the-art
debiasing methods. To improve the interpretability of our technique, we propose
a perturbation scheme in the latent space for visualizing the bias that helps
practitioners become aware of the sources of spurious correlations.