Does Representational Fairness Imply Empirical Fairness? (2022)

Abstract: NLP technologies can cause unintended harms if learned representations encode sensitive attributes of the author, or predictions systematically vary in quality across groups. Popular debiasing approaches, like adversarial training, remove sensitive information from representations in order to reduce disparate performance, however the relation between representational fairness and empirical (performance) fairness has not been systematically studied. This paper fills this gap, and proposes a novel debiasing method building on contrastive learning to encourage a latent space that separates instances based on target label, while mixing instances that share protected attributes. Our results show the effectiveness of our new method and, more importantly, show across a set of diverse debiasing methods that representational fairness does not imply empirical fairness . This work highlights the importance of aligning and understanding the relation of the optimization objective and final fairness target.

## introduction neural methods have achieved great success for text classification tasks. however, they have been trained on datasets which embody cultural and societal stereotypes from the real world, captured in spurious correlations between target labels and protected attributes. this can result in biased predictions violating empirical fairness, i.e., models perform unequally for different sub-groups. a related, but different problem occurs if representational fairness is violated which means that learned representations encode potentially sensitive author information (such as demographic information), which can be recovered by an adversarial attacker. addressing and reducing such cases of model bias figure 1 : illustration of our proposed method in the context of sentiment classification, where inputs (x) are mapped to hidden representations, which will then be used to make predictions ŷ. the points represent the instances in the latent space learned by a given model, marked with respect to sentiment and demographic labels. on the top and bottom of the gray line are hidden representations from our proposed method and a naively trained model. representational fairness is measured based on the extent to which an attacker (f ) can reconstruct protected attributes (a) from hidden representations (h). empirical fairness measures performance disparities, and measures whether model predictions are independent of protected attributes. one line of work attempts to achieve empirical fairness through learning fair representations -removing authorship-related sensitive information from learned representations -under the assumption that fair representations will naturally lead to fairer models @xcite @xcite . for example, adversarial training is a popular method which directly aims to prevent a discriminator from reverse-engineering protected attribute information from learned rep-resentations @xcite @xcite . similarly, null-space projection approaches remove protected information from hidden representations by projecting learned text representations to the null-space of linear protected attribute discriminators @xcite . in this paper, we systematically explore the interaction between fair representations and empirical fairness, both via three classes of existing approaches, as well as in considering the application of contrastive learning @xcite @xcite @xcite @xcite to fairness. contrastive learning is a natural and flexible choice of approach for representational fairness, in explicitly differentiating representations between different classes. representational fairness is achieved by learning a space which simultaneously separates instances according to their labels, while mixing instances with different protected attributes (like gender or race), either globally (section 3.2) or per class (section 3.3). our contributions in this work are: 1. we present two debiasing methods based on contrastive learning, with loss components that capture different fairness criteria; 2. based on experimental results over twitter sentiment analysis and profession classification, we show that our proposed method achieves the best representational fairness, where most baseline methods fail; 3. we show that there is no correlation between representational and empirical fairness, debunking previous assumptions about the empirical value of fair representations. ## related work we review relevant research on fairness criteria, debiasing methods, and contrastive learning. ## fair & supervised contrastive learning our method augments the objective of supervised contrastive learning to simultaneously encourage data separation in terms of the main class labels, and discourage the differentiation of data points on the basis of their protected attributes. while the method is compatible with different classifier architectures, here we use the following setup: ## experiments in this section, we report experimental results for bias mitigation. all experiments are conducted with the fairlib library @xcite , and full experimental details are provided in appendix d.