Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes
<|reference_start|>Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes: 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.<|reference_end|>
@article{hirota2024resampled,
    title={Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond
  Single Attributes},
    author={Yusuke Hirota, Jerone T. A. Andrews, Dora Zhao, Orestis
  Papakyriakopoulos, Apostolos Modas, Yuta Nakashima, Alice Xiang},
    journal={arXiv preprint arXiv:2407.03623},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03623},
    primaryClass={cs.CV}
}