arXiv:2606.11953v1 Announce Type: new Abstract: Hateful videos have become prevalent on online platforms, highlighting an urgent need for effective detection. However, existing studies primarily focus on binary classification and fail to provide contextual rationales that reveal the implicit meanings behind these judgments, significantly undermining model explainability. To fill this gap, we aim to achieve explainable hateful video detection, enabling models to provide contextual rationales that integrate relevant evidence and logical reasoning alongside decisions. This approach can comprehens
The accelerating prevalence of hateful videos on online platforms necessitates more sophisticated detection methods, prompting urgent AI research in this area.
This research provides a pathway to more explainable AI models for content moderation, which is crucial for ethical AI development and effective platform governance.
The shift from binary classification to explainable models for hateful content detection will allow for nuanced understanding and response to harmful online material.
- · Social Media Platforms
- · AI Ethics Researchers
- · Content Moderation Companies
- · Perpetrators of Hateful Content
- · AI Models Lacking Explainability
Improved detection and removal of hateful videos from online platforms.
Increased trust in AI-driven content moderation systems due to enhanced explainability.
Potential for new legal and regulatory frameworks leveraging explainable AI's contextual rationales.
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