HateXScore: 一个评估仇恨言论解释推理质量的度量套件
HateXScore: A Metric Suite for Evaluating Reasoning Quality in Hate Speech Explanations
AI总结：
HateXScore通过四个维度评估仇恨言论解释的推理质量，揭示可解释性问题，提升审核透明度
摘要：
仇恨言论检测是内容审核的关键组成部分，但当前的评估框架很少评估为何某文本被判定为仇恨言论。我们介绍了HateXScore，一个四要素度量套件，旨在评估模型解释的推理质量。它评估（i）结论的明确性，（ii）引用片段的忠实性和因果基础，（iii）受保护群体识别（政策可配置），以及（iv）这些元素之间的逻辑一致性。在六个多样化的仇恨言论数据集上进行评估，HateXScore旨在作为诊断工具，揭示标准度量如准确率或F1值无法察觉的可解释性失败和标注不一致问题。此外，人类评估显示与HateXScore有很强的一致性，证明其作为可靠和透明审核的实用工具。
英文摘要：
Hateful speech detection is a key component of content moderation, yet current evaluation frameworks rarely assess why a text is deemed hateful. We introduce \textsf{HateXScore}, a four-component metric suite designed to evaluate the reasoning quality of model explanations. It assesses (i) conclusion explicitness, (ii) faithfulness and causal grounding of quoted spans, (iii) protected group identification (policy-configurable), and (iv) logical consistency among these elements. Evaluated on six diverse hate speech datasets, \textsf{HateXScore} is intended as a diagnostic complement to reveal interpretability failures and annotation inconsistencies that are invisible to standard metrics like Accuracy or F1. Moreover, human evaluation shows strong agreement with \textsf{HateXScore}, validating it as a practical tool for trustworthy and transparent moderation. \textcolor{red}{Disclaimer: This paper contains sensitive content that may be disturbing to some readers.}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
