BLADE: Bias-Linked Adaptive DEbiasing

2025-10-05

AI Summary

Neural networks are vulnerable to spurious correlations—i.e., latent biases—in training data, leading to degraded generalization. Existing debiasing methods typically rely on either prior knowledge of bias types or bias-conflicting samples, assumptions often impractical in real-world settings. To address this, we propose a prior-free generative debiasing framework comprising three key components: (1) cross-bias-domain image translation via a generative model; (2) an adaptive image augmentation mechanism guided by a bias-aware optimization objective; and (3) explicit feature alignment and misalignment strategies that strengthen task-relevant representations while suppressing bias-correlated ones. Evaluated on benchmarks including worst-group accuracy on CIFAR-10, our method achieves approximately 18% improvement over state-of-the-art approaches, significantly advancing unsupervised debiasing research.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Neural networks have revolutionized numerous fields, yet they remain vulnerable to a critical flaw: the tendency to learn implicit biases, spurious correlations between certain attributes and target labels in training data. These biases are often more prevalent and easier to learn, causing models to rely on superficial patterns rather than task-relevant features necessary for generalization. Existing methods typically rely on strong assumptions, such as prior knowledge of these biases or access to bias-conflicting samples, i.e., samples that contradict spurious correlations and counterbalance bias-aligned samples, samples that conform to these spurious correlations. However, such assumptions are often impractical in real-world settings. We propose BLADE ({B}ias-{L}inked {A}daptive {DE}biasing), a generative debiasing framework that requires no prior knowledge of bias or bias-conflicting samples. BLADE first trains a generative model to translate images across bias domains while preserving task-relevant features. Then, it adaptively refines each image with its synthetic counterpart based on the image's susceptibility to bias. To encourage robust representations, BLADE aligns an image with its bias-translated synthetic counterpart that shares task-relevant features but differs in bias, while misaligning it with samples sharing the same bias. We evaluate BLADE on multiple benchmark datasets and show that it significantly outperforms state-of-the-art methods. Notably, it exceeds the closest baseline by an absolute margin of around 18% on the corrupted CIFAR-10 dataset under the worst group setting, establishing a new benchmark in bias mitigation and demonstrating its potential for developing more robust deep learning models without explicit supervision.

Problem

• Neural networks learn spurious correlations from biased training data
• Existing debiasing methods require impractical prior bias knowledge
• BLADE mitigates biases without requiring bias labels or conflicting samples

Innovation

• Generative model translates images across bias domains
• Adaptively refines images based on bias susceptibility
• Aligns images with bias-translated synthetic counterparts