  Existing bias mitigation methods for DNN models primarily work on learning
debiased encoders. This process not only requires a lot of instance-level
annotations for sensitive attributes, it also does not guarantee that all
fairness sensitive information has been removed from the encoder. To address
these limitations, we explore the following research question: Can we reduce
the discrimination of DNN models by only debiasing the classification head,
even with biased representations as inputs? To this end, we propose a new
mitigation technique, namely, Representation Neutralization for Fairness (RNF)
that achieves fairness by debiasing only the task-specific classification head
of DNN models. To this end, we leverage samples with the same ground-truth
label but different sensitive attributes, and use their neutralized
representations to train the classification head of the DNN model. The key idea
of RNF is to discourage the classification head from capturing spurious
correlation between fairness sensitive information in encoder representations
with specific class labels. To address low-resource settings with no access to
sensitive attribute annotations, we leverage a bias-amplified model to generate
proxy annotations for sensitive attributes. Experimental results over several
benchmark datasets demonstrate our RNF framework to effectively reduce
discrimination of DNN models with minimal degradation in task-specific
performance.
