ID-XCB: Data-independent Debiasing for Fair and Accurate Transformer-based Cyberbullying Detection
<|reference_start|>ID-XCB: Data-independent Debiasing for Fair and Accurate Transformer-based Cyberbullying Detection: Swear words are a common proxy to collect datasets with cyberbullying incidents. Our focus is on measuring and mitigating biases derived from spurious associations between swear words and incidents occurring as a result of such data collection strategies. After demonstrating and quantifying these biases, we introduce ID-XCB, the first data-independent debiasing technique that combines adversarial training, bias constraints and debias fine-tuning approach aimed at alleviating model attention to bias-inducing words without impacting overall model performance. We explore ID-XCB on two popular session-based cyberbullying datasets along with comprehensive ablation and generalisation studies. We show that ID-XCB learns robust cyberbullying detection capabilities while mitigating biases, outperforming state-of-the-art debiasing methods in both performance and bias mitigation. Our quantitative and qualitative analyses demonstrate its generalisability to unseen data.<|reference_end|>
@article{yi2024id-xcb:,
    title={ID-XCB: Data-independent Debiasing for Fair and Accurate
  Transformer-based Cyberbullying Detection},
    author={Peiling Yi and Arkaitz Zubiaga},
    journal={arXiv preprint arXiv:2402.16458},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16458},
    primaryClass={cs.CL}
}