@inproceedings{lu-etal-2024-take,
    title = "Take Its Essence, Discard Its Dross! Debiasing for Toxic Language Detection via Counterfactual Causal Effect",
    author = "Lu, Junyu  and
      Xu, Bo  and
      Zhang, Xiaokun  and
      Liu, Kaiyuan  and
      Zhang, Dongyu  and
      Yang, Liang  and
      Lin, Hongfei",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1353",
    pages = "15566--15578",
    abstract = "Researchers have attempted to mitigate lexical bias in toxic language detection (TLD). However, existing methods fail to disentangle the {``}useful{''} and {``}misleading{''} impact of lexical bias on model decisions. Therefore, they do not effectively exploit the positive effects of the bias and lead to a degradation in the detection performance of the debiased model. In this paper, 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.",
}

Researchers have attempted to mitigate lexical bias in toxic language detection (TLD). However, existing methods fail to disentangle the {``}useful{''} and {``}misleading{''} impact of lexical bias on model decisions. Therefore, they do not effectively exploit the positive effects of the bias and lead to a degradation in the detection performance of the debiased model. In this paper, 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.
Lu, Junyu Xu, Bo Zhang, Xiaokun Liu, Kaiyuan Zhang, Dongyu Yang, Liang Lin, Hongfei
Take Its Essence, Discard Its Dross! Debiasing for Toxic Language Detection via Counterfactual Causal Effect