Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes (2024)

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. Specifically, we achieve an average societal bias reduction of 46.1% in leakage-based bias metrics for multi-label classification and 74.8% for image captioning.

## introduction models trained on biased data can develop prediction rules based on spurious correlations (i.e., associations devoid of causal relationships), perpetuating and amplifying harmful stereotypes @xcite . for example, image captioning models may generate gendered captions by associating gender with depicted activities @xcite , location @xcite , or objects @xcite . dataset-level bias mitigation aims to reduce spurious correlations between labeled image attributes (e.g., teddy bear) and protected groups (e.g., woman). resampling approaches balance the co-occurrence of each attribute with each group @xcite . however, models can still exploit correlations between groups and sets of attributes (e.g., man with {dog, pizza, couch}), even when individual attributes are balanced @xcite . moreover, spurious correlations extend to unlabeled attributes, which current strategies do not address-e.g., gender disparities in image color statistics @xcite or the person-toobject spatial distances @xcite . while equal group distributions in real-world datasets are challenging to achieve, generative textto-image models now enable targeted image modifications @xcite @xcite . for example, bias detection methods alter image subjects' appearance to assess counterfactual fairness @xcite or model bias @xcite . however, manipulating individuals' appearances without consent raises significant ethical and privacy concerns @xcite @xcite @xcite . to address these challenges, we create training datasets with text-guided inpainting @xcite , ensuring attribute distributions are independent of protected groups. using masked person images and text prompts, we generate counterfactual images by inpainting only the masked regions, addressing ethical concerns of altering nonconsensual persons and ensuring equal representation of protected groups across attributes. we introduce data filters to mitigate biases from generative text-guided inpainting models @xcite @xcite , evaluating images based on adherence to prompts, preservation of attributes and semantics, and color fidelity, validated by human evaluators. unlike prior work @xcite @xcite , training on our counterfactual data decorrelates both labeled and unlabeled attributes from protected groups without impacting model performance. comprehensive evaluations show our approach significantly reduces prediction rules based on spurious correlations in multi-label classification and image captioning across various architectures (e.g., resnet-50 @xcite , swin transformer @xcite ), datasets (coco @xcite , openimages @xcite ), and protected groups (gender, skin tone). our key contributions are summarized as follows: ## method we create training datasets with group-independent image attribute distributions by using masked person images and text prompts with an off-the-shelf diffusion model, as outlined in figure 2 . ## experiments building on prior research @xcite @xcite @xcite @xcite , we evaluate our synthetic dataset creation method on multi-label image classification and image captioning tasks using quantitative metrics, human studies, qualitative comparisons, and effectiveness analysis. evaluations are conducted on test sets of real data. and if the second largest person exceeds 55,000 pixels, we also inpaint that region, using the person label for coco. for image generation, we create @xmath1.g., woman, man) using guidance scales of 7.5, 9.5, and 15.0 to ensure diversity. filter weights are set to 1 (i.e., c @xmath2, contributing equally. results are based on five models trained with different random seeds. more details are in appendices a and b. ## conclusion we present a dataset-level bias mitigation pipeline that effectively reduces gender and skin tone biases by ensuring group-independent attribute distribution using synthetic-only images. our findings indicate that mixing real and synthetic images introduces spurious correlations, underscoring the need for caution when augmenting datasets with synthetic data. our work highlights the potential of synthetic data in bias mitigation and suggests further exploration into optimizing synthetic data generation and integration techniques for increased bias reduction.