HateXScore: A Metric Suite for Evaluating Reasoning Quality in Hate Speech Explanations
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| Format: | Preprint |
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2026
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| _version_ | 1866914265673236480 |
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| author | Hu, Yujia Lee, Roy Ka-Wei |
| author_facet | Hu, Yujia Lee, Roy Ka-Wei |
| contents | 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.} |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_13547 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HateXScore: A Metric Suite for Evaluating Reasoning Quality in Hate Speech Explanations Hu, Yujia Lee, Roy Ka-Wei Computation and Language Artificial Intelligence 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.} |
| title | HateXScore: A Metric Suite for Evaluating Reasoning Quality in Hate Speech Explanations |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2601.13547 |