Fairness for Text Classification Tasks with Identity Information Data Augmentation Methods

TL;DR

This paper introduces a two-step data augmentation method that creates comprehensive identity pairs and applies them to improve counterfactual fairness in text classification tasks, leading to more diverse training data and better fairness metrics.

Contribution

A novel two-stage data augmentation process for generating diverse identity pairs to enhance counterfactual fairness in text classification.

Findings

Improved counterfactual fairness metric scores on benchmark tasks.

Generated more diverse identity pairs for training data.

Enhanced fairness without sacrificing classification performance.

Abstract

Counterfactual fairness methods address the question: How would the prediction change if the sensitive identity attributes referenced in the text instance were different? These methods are entirely based on generating counterfactuals for the given training and test set instances. Counterfactual instances are commonly prepared by replacing sensitive identity terms, i.e., the identity terms present in the instance are replaced with other identity terms that fall under the same sensitive category. Therefore, the efficacy of these methods depends heavily on the quality and comprehensiveness of identity pairs. In this paper, we offer a two-step data augmentation process where (1) the former stage consists of a novel method for preparing a comprehensive list of identity pairs with word embeddings, and (2) the latter consists of leveraging prepared identity pairs list to enhance the training instances by applying three simple operations (namely identity pair replacement, identity term blindness, and identity pair swap). We empirically show that the two-stage augmentation process leads to diverse identity pairs and an enhanced training set, with an improved counterfactual token-based fairness metric score on two well-known text classification tasks.