Exploring the Plausibility of Hate and Counter Speech Detectors with Explainable AI
<|reference_start|>Exploring the Plausibility of Hate and Counter Speech Detectors with Explainable AI: In this paper we investigate the explainability of transformer models and their plausibility for hate speech and counter speech detection. We compare representatives of four different explainability approaches, i.e., gradient-based, perturbation-based, attention-based, and prototype-based approaches, and analyze them quantitatively with an ablation study and qualitatively in a user study. Results show that perturbation-based explainability performs best, followed by gradient-based and attention-based explainability. Prototypebased experiments did not yield useful results. Overall, we observe that explainability strongly supports the users in better understanding the model predictions.<|reference_end|>
@article{böck2024exploring,
    title={Exploring the Plausibility of Hate and Counter Speech Detectors with
  Explainable AI},
    author={Adrian Jaques B"ock, Djordje Slijepv{c}evi'c, Matthias Zeppelzauer},
    journal={arXiv preprint arXiv:2407.20274},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20274},
    primaryClass={cs.LG cs.AI cs.CL}
}