Counterfactual Fairness in Text Classification through Robustness

TL;DR

This paper introduces a new metric and methods to improve counterfactual fairness in text classifiers, ensuring predictions are less biased by sensitive attributes without sacrificing performance.

Contribution

It proposes the counterfactual token fairness metric and three training approaches, bridging robustness and fairness in text classification.

Findings

Blindness and CLP improve counterfactual fairness

Methods do not reduce classifier accuracy

Tradeoffs exist between fairness and group fairness

Abstract

In this paper, we study counterfactual fairness in text classification, which asks the question: How would the prediction change if the sensitive attribute referenced in the example were different? Toxicity classifiers demonstrate a counterfactual fairness issue by predicting that "Some people are gay" is toxic while "Some people are straight" is nontoxic. We offer a metric, counterfactual token fairness (CTF), for measuring this particular form of fairness in text classifiers, and describe its relationship with group fairness. Further, we offer three approaches, blindness, counterfactual augmentation, and counterfactual logit pairing (CLP), for optimizing counterfactual token fairness during training, bridging the robustness and fairness literature. Empirically, we find that blindness and CLP address counterfactual token fairness. The methods do not harm classifier performance, and have varying tradeoffs with group fairness. These approaches, both for measurement and optimization, provide a new path forward for addressing fairness concerns in text classification.