Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929417413984256 |
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| author | Hirota, Yusuke Andrews, Jerone T. A. Zhao, Dora Papakyriakopoulos, Orestis Modas, Apostolos Nakashima, Yuta Xiang, Alice |
| author_facet | Hirota, Yusuke Andrews, Jerone T. A. Zhao, Dora Papakyriakopoulos, Orestis Modas, Apostolos Nakashima, Yuta Xiang, Alice |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03623 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes Hirota, Yusuke Andrews, Jerone T. A. Zhao, Dora Papakyriakopoulos, Orestis Modas, Apostolos Nakashima, Yuta Xiang, Alice Computer Vision and Pattern Recognition 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. |
| title | Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.03623 |