Controlling Bias Exposure for Fair Interpretable Predictions

• URL: http://arxiv.org/abs/2210.07455v1
• Date: Fri, 14 Oct 2022 01:49:01 GMT
• Title: Controlling Bias Exposure for Fair Interpretable Predictions
• Authors: Zexue He, Yu Wang, Julian McAuley, Bodhisattwa Prasad Majumder
• Abstract summary: We argue that a favorable debiasing method should use sensitive information 'fairly' rather than blindly eliminating it. Our model achieves a desirable trade-off between debiasing and task performance along with producing debiased rationales as evidence.
• Score: 11.364105288235308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent work on reducing bias in NLP models usually focuses on protecting or isolating information related to a sensitive attribute (like gender or race). However, when sensitive information is semantically entangled with the task information of the input, e.g., the gender information is predictive for a profession, a fair trade-off between task performance and bias mitigation is difficult to achieve. Existing approaches perform this trade-off by eliminating bias information from the latent space, lacking control over how much bias is necessarily required to be removed. We argue that a favorable debiasing method should use sensitive information 'fairly' rather than blindly eliminating it (Caliskan et al., 2017; Sun et al., 2019). In this work, we provide a novel debiasing algorithm by adjusting the predictive model's belief to (1) ignore the sensitive information if it is not useful for the task; (2) use sensitive information minimally as necessary for the prediction (while also incurring a penalty). Experimental results on two text classification tasks (influenced by gender) and an open-ended generation task (influenced by race) indicate that our model achieves a desirable trade-off between debiasing and task performance along with producing debiased rationales as evidence.

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