Fairness via Representation Neutralization

TL;DR

This paper introduces Representation Neutralization for Fairness (RNF), a method that reduces bias in deep neural networks by debiasing only the classification head, avoiding the need for extensive sensitive attribute annotations.

Contribution

RNF is a novel approach that neutralizes sensitive information in the classification head, enabling bias mitigation without modifying the encoder or requiring sensitive attribute labels.

Findings

Effectively reduces discrimination in DNN models.

Maintains high task-specific performance.

Works in low-resource settings with proxy annotations.

Abstract

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.