HateXScore: A Metric Suite for Evaluating Reasoning Quality in Hate Speech Explanations

Hateful speech detection is a key component of content moderation, yet current evaluation frameworks rarely assess why a text is deemed hateful.We introduce 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, 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 Ha-teXScore, validating it as a practical tool for trustworthy and transparent moderation.