Counterfactual Fairness in Text Classification through Robustness
This work addresses fairness concerns in text classification, particularly for toxicity detection, by providing new measurement and optimization methods, though it is incremental in bridging robustness and fairness literature.
The paper tackles counterfactual fairness in text classification by introducing a metric called counterfactual token fairness (CTF) and three training approaches (blindness, counterfactual augmentation, and counterfactual logit pairing) to optimize it, finding that blindness and CLP effectively address fairness without harming classifier performance.
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.