Pinpoint Counterfactuals: Reducing social bias in foundation models via localized counterfactual generation

Foundation models trained on web-scraped datasets propagate societal biases to downstream tasks. While counterfactual generation enables bias analysis, existing methods introduce artifacts by modifying contextual elements like clothing and background. We present a localized counterfactual generation...

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Bibliographic Details
Published in:arXiv.org 2024-12
Main Authors: Sirotkin, Kirill, Escudero-Viñolo, Marcos, Carballeira, Pablo, Maniparambil, Mayug, Barata, Catarina, O'Connor, Noel E
Format: Article
Language:English
Subjects:
Datasets
Human bias
Human performance
Online Access:Get full text
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Summary:Foundation models trained on web-scraped datasets propagate societal biases to downstream tasks. While counterfactual generation enables bias analysis, existing methods introduce artifacts by modifying contextual elements like clothing and background. We present a localized counterfactual generation method that preserves image context by constraining counterfactual modifications to specific attribute-relevant regions through automated masking and guided inpainting. When applied to the Conceptual Captions dataset for creating gender counterfactuals, our method results in higher visual and semantic fidelity than state-of-the-art alternatives, while maintaining the performance of models trained using only real data on non-human-centric tasks. Models fine-tuned with our counterfactuals demonstrate measurable bias reduction across multiple metrics, including a decrease in gender classification disparity and balanced person preference scores, while preserving ImageNet zero-shot performance. The results establish a framework for creating balanced datasets that enable both accurate bias profiling and effective mitigation.
ISSN:2331-8422