Researchers Develop Framework for Explainable Detection of Hateful Videos with Contextual Reasoning
Computer scientists have introduced a new framework called IARE that detects hateful videos while providing explanations for its decisions, addressing a gap in existing detection systems that only perform binary classification. The research includes two new annotated datasets (Ex-HateMM and Ex-ImpliHateVid) with fine-grained labels of harmful multimodal elements and contextual rationales. This work matters because explainable AI systems can improve content moderation transparency and help platforms better understand why content is flagged as harmful.
Researchers have developed an Information Augmentation and Reasoning Enhancement (IARE) framework designed to detect hateful videos while simultaneously generating human-readable explanations for detection decisions. The framework addresses a significant limitation in current approaches, which typically only classify videos as hateful or not without providing contextual reasoning. The study introduces two new datasets—Ex-HateMM and Ex-ImpliHateVid—that provide fine-grained annotations of multimodal harmful elements alongside contextual rationales explaining why content is considered hateful. The IARE framework operates in two phases: an information augmentation phase that uses multimodal chain-of-thought reasoning to integrate harmful elements, and a reasoning enhancement phase that uses Direct Preference Optimization to guide the model toward logically coherent justifications. Experimental results demonstrate that the framework achieves state-of-the-art performance while generating accurate explanations, potentially improving transparency in content moderation systems.
What's missing
The paper does not discuss potential limitations of the approach, such as how the framework handles edge cases, cultural or contextual variations in what constitutes 'hateful' content, or computational resource requirements. Additionally, there is no discussion of how the datasets were created, potential annotation biases, or inter-annotator agreement metrics.
What different sources said
- arXiv cs.CLCenter
Decoding Multimodal Cues: Unveiling the Implicit Meaning Behind Hateful Videos
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