Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss

• URL: http://arxiv.org/abs/2305.19671v1
• Date: Wed, 31 May 2023 09:09:59 GMT
• Title: Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss
• Authors: Moritz Vandenhirtz, Laura Manduchi, Ri\v{c}ards Marcinkevi\v{c}s and Julia E. Vogt
• Abstract summary: neural networks are notorious for learning unwanted associations, also known as biases, instead of the underlying decision rule. We propose Signal is Harder, a variational-autoencoder-based method that simultaneously trains a biased and unbiased classifier. We propose a perturbation scheme in the latent space for visualizing the bias that helps practitioners become aware of the sources of spurious correlations.
• Score: 10.031357641396616
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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