Training-Time Explainability for Multilingual Hate Speech Detection: Aligning Model Reasoning with Human Rationales

Abstract:Online hate against Muslim communities often appears in culturally coded, multilingual forms that evade conventional AI moderation. Such systems, though accurate, remain opaque and risk bias, over-censorship, or under-moderation, particularly when detached from sociocultural context. We propose a \emph{training-time} explainability framework that aligns model reasoning with human-annotated rationales, improving both classification performance and interpretability. Our approach is evaluated on HateXplain (English) and BullySent (Hinglish), reflecting the prevalence of anti-Muslim hate across both languages. Using LIME, Integrated Gradients, Grad X Input, and attention, we assess accuracy, explanation quality, and cross-method agreement. Results show that gradient- and attention-based regularization improve F-scores, enhance plausibility and faithfulness, and capture culturally specific cues for detecting implicit anti-Muslim hate, offering a path toward multilingual, culturally aware content moderation.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.26125 [cs.CL]

(or arXiv:2608.26125v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.26125 Focus to learn more arXiv-issued DOI via DataCite

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