Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization
This addresses bias mitigation in NLP for downstream applications without requiring protected attribute labels, though it is incremental over prior debiasing techniques.
The paper tackled the problem of language models reintroducing biases during fine-tuning on downstream tasks, and introduced a class-wise variance regularization method that outperformed existing debiasing baselines while maintaining task performance.
Language models frequently inherit societal biases from their training data. Numerous techniques have been proposed to mitigate these biases during both the pre-training and fine-tuning stages. However, fine-tuning a pre-trained debiased language model on a downstream task can reintroduce biases into the model. Additionally, existing debiasing methods for downstream tasks either (i) require labels of protected attributes (e.g., age, race, or political views) that are often not available or (ii) rely on indicators of bias, which restricts their applicability to gender debiasing since they rely on gender-specific words. To address this, we introduce a novel debiasing regularization technique based on the class-wise variance of embeddings. Crucially, our method does not require attribute labels and targets any attribute, thus addressing the shortcomings of existing debiasing methods. Our experiments on encoder language models and three datasets demonstrate that our method outperforms existing strong debiasing baselines that rely on target attribute labels while maintaining performance on the target task.