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arXiv · 2407.03623

Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes

Yusuke Hirota·Jerone T. A. Andrews·Dora Zhao·Orestis Papakyriakopoulos·Apostolos Modas·Yuta Nakashima·Alice Xiang
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Abstract

We tackle societal bias in image-text datasets by removing spurious correlations between protected groups and image attributes. Traditional methods only target labeled attributes, ignoring biases from unlabeled ones. Using text-guided inpainting models, our approach ensures protected group independence from all attributes and mitigates inpainting biases through data filtering. Evaluations on multi-label image classification and image captioning tasks show our method effectively reduces bias without compromising performance across various models.

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Yusuke Hirota, Jerone T. A. Andrews, Dora Zhao, Orestis Papakyriakopoulos, Apostolos Modas, Yuta Nakashima, Alice Xiang. 2024-07-04. Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes. https://arxiv.org/abs/2407.03623

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