Online safety in low-resource languages hinges not only on accurate hate speech detection but also on transparent, culturally grounded explanations. Yet prior works in Bangla largely focus on hate classification, while overlooking interpretability. We address this gap by introducing BanHADEX, the first hate explainability dataset in Bangla with human-annotated labels. BanHADEX contains 19,203 YouTube comments spanning April 2024–June 2025, annotated for binary hate classification with seven fine-grained hate categories, seven target groups, and concise explanations for each sample. Our data pipeline relies on a two-stage annotation protocol that uses majority voting for robust labeling. Our rich suite of experiments on open and closed-source LLMs reveals that explanation-guided LoRA substantially outperforms both classification and explanation quality across prompting and fine-tuning strategies. BanHADEX establishes the groundworks for faithful interpretability and safer moderation in linguistically rich yet under-resourced languages.

Online safety in low-resource languages relies on effective hate speech detection, yet Bangla remains critically underexplored. Existing resources focus narrowly on binary classification and fail to capture the evolving, implicit nature of online hate. To address this, we introduce BanHate, a large-scale Bangla hate speech dataset, comprising 19,203 YouTube comments collected between April 2024 and June 2025. Each comment is annotated for binary hate labels, seven fine-grained categories, and seven target groups, reflecting diverse forms of abuse in contemporary Bangla discourse. We develop a tailored pipeline for data collection, filtering, and annotation with majority voting to ensure reliability. To benchmark BanHate, we evaluate a diverse set of open- and closed-source large language models under prompting and LoRA fine-tuning. We find that LoRA substantially improves open-source models, while closed-source models, such as GPT-4o and Gemini, achieve strong performance in binary hate classification, but face challenges in detecting implicit and fine-grained hate. BanHate sets a new benchmark for Bangla hate speech research, providing a foundation for safer moderation in low-resource languages. Our dataset is available at: https://huggingface.co/datasets/aplycaebous/BanHate.