Take its Essence, Discard its Dross! Debiasing for Toxic Language Detection via Counterfactual Causal Effect
This addresses bias issues in toxic language detection models, offering a more nuanced debiasing approach that is incremental over existing methods.
The paper tackles lexical bias in toxic language detection by proposing a counterfactual causal debiasing framework that preserves useful impacts while eliminating misleading ones, achieving state-of-the-art performance in accuracy and fairness with improved generalization on out-of-distribution data.
Current methods of toxic language detection (TLD) typically rely on specific tokens to conduct decisions, which makes them suffer from lexical bias, leading to inferior performance and generalization. Lexical bias has both "useful" and "misleading" impacts on understanding toxicity. Unfortunately, instead of distinguishing between these impacts, current debiasing methods typically eliminate them indiscriminately, resulting in a degradation in the detection accuracy of the model. To this end, we propose a Counterfactual Causal Debiasing Framework (CCDF) to mitigate lexical bias in TLD. It preserves the "useful impact" of lexical bias and eliminates the "misleading impact". Specifically, we first represent the total effect of the original sentence and biased tokens on decisions from a causal view. We then conduct counterfactual inference to exclude the direct causal effect of lexical bias from the total effect. Empirical evaluations demonstrate that the debiased TLD model incorporating CCDF achieves state-of-the-art performance in both accuracy and fairness compared to competitive baselines applied on several vanilla models. The generalization capability of our model outperforms current debiased models for out-of-distribution data.