Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness (2021)

Abstract: Text style can reveal sensitive attributes of the author (e.g. age and race) to the reader, which can, in turn, lead to privacy violations and bias in both human and algorithmic decisions based on text. For example, the style of writing in job applications might reveal protected attributes of the candidate which could lead to bias in hiring decisions, regardless of whether hiring decisions are made algorithmically or by humans. We propose a VAE-based framework that obfuscates stylistic features of human-generated text through style transfer, by automatically re-writing the text itself. Critically, our framework operationalizes the notion of obfuscated style in a flexible way that enables two distinct notions of obfuscated style: (1) a minimal notion that effectively intersects the various styles seen in training, and (2) a maximal notion that seeks to obfuscate by adding stylistic features of all sensitive attributes to text, in effect, computing a union of styles. Our style-obfuscation framework can be used for multiple purposes, however, we demonstrate its effectiveness in improving the fairness of downstream classifiers. We also conduct a comprehensive study on style-pooling’s effect on fluency, semantic consistency, and attribute removal from text, in two and three domain style transfer.

## introduction machine learning (ml) algorithms are used in a wide range of tasks, including high-stakes applications like determining credit ratings, setting insurance policy rates, making hiring decisions, and performing facial recognition. it has been shown that such algorithms can produce outcomes that are biased towards a certain gender or race @xcite @xcite . ideally, high-stakes decisions made by either humans or ml algorithms, should not be influenced by irrelevant, protected attributes like nationality, age, or gender. in many instances, the input data used for making high-stakes decisions is text that is authored by a human candidate -for example, hiring decisions are often based on bios and personal statements. recent work @xcite shows that automatic hiring-decision models trained on bios are less likely to select female candidates for certain roles (e.g. architect, software engineer, and surgeon) even when the gender of the author is not explicitly provided to the system. bias is, of course, not limited to algorithmic decisions, humans make biased decisions based on text, even when the protected attributes of the author are not explicitly revealed @xcite . together, these results indicate that both algorithms and humans can (1) decipher protected attributes of authors based on stylistic features of text, and (2) whether consciously or not, be biased by this information. a large body of prior work has attempted to address algorithmic bias by modifying different stages of the natural language processing (nlp) pipeline. for example, @xcite attempt to de-bias word embeddings used by nlp systems, while @xcite address the bias in learned model representations and encodings. while effective in many cases, such approaches do nothing to mitigate bias in decisions made by humans based on text. we propose a fundamentally different approach. rather than mitigating bias in learning algorithms that make decisions based on text, we propose a framework that obfuscates stylistic features of human-generated text by automatically re-writing the text itself. by obfuscating stylistic features, readers (human or algorithms) will be less able to infer protected attributes that enable bias. we introduce a novel framework that enables 'style pooling': the automatic transduction of user-generated text to a central, obfuscated style. notions of 'centrality' can themselves introduce bias -for example, a system might learn to obfuscate by mapping all text to the dominant style ## proposed method in this section, we first introduce our model structure, then describe our style-pooling priors and the unsupervised learning and inference techniques we leverage for this model class. finally, we introduce our style de-boosting mechanism. ## experimental setup here, we provide a brief description of our experimental setup. our code, data and model checkpoints are uploaded in the supplementary material. more details on the code, model configurations, datasets and hyperparameters are provided in appendix sections a.1, a.2, a.3 and a.4. ## conclusion we proposed a probabilistic vae framework for automatically re-writing text in order to obfuscate stylistic features that might reveal sensitive attributes of the author. we demonstrated in experiments that our proposed framework can indeed reduce bias in downstream text classification. finally, our model poses two ways of defining a central style. future work might consider further explorations of alternative notions of stylistic centrality.