Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial Attack (2021)

Abstract: Representation learning is widely used in NLP for a vast range of tasks. However, representations derived from text corpora often reflect social biases. This phenomenon is pervasive and consistent across different neural models, causing serious concern. Previous methods mostly rely on a pre-specified, user-provided direction or suffer from unstable training. In this paper, we propose an adversarial disentangled debiasing model to dynamically decouple social bias attributes from the intermediate representations trained on the main task. We aim to denoise bias information while training on the downstream task, rather than completely remove social bias and pursue static unbiased representations. Experiments show the effectiveness of our method, both on the effect of debiasing and the main task performance.

## introduction supervised neural networks have achieved remarkable success in a wide range of natural language processing (nlp) tasks. the fundamental capability of these neural models is to learn effective feature representations @xcite for the downstream prediction task. unfortunately, the learned representations frequently contain undesirable biases with respect to things that we would rather not use for decision making. we refer to such inappropriate factors as protected attributes @xcite . biased information has serious real-world consequences. for example, concerns have been raised about automatic resume filtering systems giving preference to male applicants when the only distinguishing factor is the applicants' gender @xcite . in this paper, we focus on social bias, such as gender bias which is the preference or prejudice towards one gender over the other @xcite , race bias and age bias. from the perspective of the debiasing target, previous debiasing works can be approximately classified into two types, word embedding @xcite @xcite @xcite and sentence embedding @xcite @xcite . the former aims to reduce the gender bias in word embedding, either as a post-processing step @xcite or as part of the training procedure @xcite . the latter focuses on removing these protected attributes from the downstream intermediate representations @xcite . in this paper, we consider the latter setting and focus on how to mitigate undesirable social bias from the encoded representations without hurting the performance of the main task. in terms of debiasing methods, previous models are either based on projection on a pre-specified, user-provided direction @xcite or null-space @xcite , or on adding an additional gender discriminator @xcite . the former first trains an intermediate feature extractor on the main task, then using a separate projection method to remove social bias from the representations, finally fine-tuning on the main task. the debiasing procedure can be regarded as static because of no direct interaction between the main task and the debiasing task. therefore, these methods have no guarantee that the representations for predicting the main task do not contain any bias information. existing work, @xcite , has shown that these methods only cover up the bias and that in fact, the information is deeply ingrained in the representations. compared to these static debiasing methods, gender discriminator based methods @xcite use the traditional generative adversarial network (gan) @xcite to distinguish protected gender attributes from encoded representations. however, they are notoriously hard to train @xcite . @xcite has shown that the complete removal of the protected information is nontrivial: even when the attribute seems protected, different classifiers of the same architecture can often still succeed in extracting it. hence, we aim to dynamically disentangle the social bias from the encoded representations while jointly training on the main task in a more stable way, rather than directly remove protected attributes. in fact, we show that bias information always remains even after adversarial debiasing and can be reconstructed from the encoded representations. the main goal of debiasing is to prevent downstream models from utilizing these social bias in the representations, that is, dynamic disentanglement instead of complete removal, as fig 1 displays . in this paper, we propose an adversarial disentangled debiasing model to dynamically decouple social bias attributes from the intermediate representations trained on the main task. our motivation is to denoise bias information while training on the downstream task, rather than completely remove social bias and pursue static unbiased representations. previous works @xcite show that even debiasing models achieve high fairness @xcite , a fair amount of protected information still remains and can be extracted from the encoded representations. we argue that one can hardly re- embeddings attentive pooling classifer (main task) classifer (main task) classifer (protected) classifer (protected) l main l protected input text normalize scale gradient of l protected w.r.t embeddings debiasing protected classifier boundary 1. protected forward 2. debiasing backward 3. main task forward context encoder 4. update parameters move all gender or race directions in the latent space but only preserve bias-free prediction on the downstream task. specifically, we use a protected attribute classifier to generate model-agnostic adversarial worst-case perturbations to the representations in the direction that significantly increases the classifier's loss. then we apply the perturbations to train the model of the downstream task end-toend. the main difference between our method and gan-based counterparts is that gans suffer from unstable training for the two-stage min-max procedure but our method directly computes gradientbased perturbations to disentangle bias information from the representations. we hope to provide new insights and directions towards solving social bias issues. foot_0 2 approach ## related work the objective of controlled removal of specific types of information from neural representations is tightly related to the task of disentanglement of the representations @xcite , that is, controlling and separating the different kinds of information encoded in them. previous models are either based on projection on a pre-specified, user-provided direction @xcite or null-space @xcite , or adding an additional gender discriminator @xcite , or the impact of data decisions @xcite . the former first train an intermediate feature extractor on the main task, then use a separate projection method to remove social bias from the representations, finally finetune on the main task. compared to these static debiasing methods, gender discriminator based methods @xcite use the traditional generative adversarial network (gan) @xcite to remove protected gender attributes from encoded representations. however, they are notoriously hard to train @xcite . @xcite has shown that the complete removal of the protected information is nontrivial: even when the attribute seems protected, different classifiers of the same architecture can often still succeed in extracting it. therefore, in this paper, we aim to dynamically disentangle the social bias from the encoded representations while jointly training on the main task in a more stable way, rather than directly remove protected attributes. the main goal of debiasing is to prevent downstream models from utilizing these social biases in the representations, that is, dynamic disentanglement instead of complete removal. ## conclusion in this paper, we focus on removing social bias in representation learning. we argue that the main goal of debiasing is to prevent downstream models from utilizing these social biases in the representation, that is, dynamic disentanglement instead of complete removal. therefore, we propose an adversarial disentangled debiasing model to dynamically decouple social bias attributes from the intermediate representation trained on the main task. we perform extensive experiments and analysis to demonstrate the effectiveness of our method. we hope to provide new insights and directions towards solving social bias.