  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., 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; Bogen et al., 2020). 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.
