Exploring the Plausibility of Hate and Counter Speech Detectors with Explainable AI

• URL: http://arxiv.org/abs/2407.20274v1
• Date: Thu, 25 Jul 2024 10:17:04 GMT
• Title: Exploring the Plausibility of Hate and Counter Speech Detectors with Explainable AI
• Authors: Adrian Jaques Böck, Djordje Slijepčević, Matthias Zeppelzauer,
• Abstract summary: We compare four different explainability approaches, i.e. gradient-based, perturbation-based, attention-based, and prototype-based approaches. Results show that perturbation-based explainability performs best, followed by gradient-based and attention-based explainability.
• Score: 0.7317046947172644
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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