多数票压制少数价值观：HateXplain中仇恨/冒犯边界的标注者分歧
Majority Vote Silences Minority Values: Annotator Disagreement at the Hate/Offensive Boundary in HateXplain
AI总结：
研究发现HateXplain中42.6%的标注者分歧集中在仇恨/冒犯边界，多数投票掩盖了这种争议，导致模型在边界案例上准确率下降22-28个百分点，且标准评估无法检测。
摘要：
仇恨言论标注流程通常在训练前将标注者分歧汇总为多数投票标签。我们表明这种汇总并非中立：HateXplain中所有标注者分歧的42.6%具体集中在仇恨/冒犯边界，这一模式与标注者对仇恨起点应用不同阈值一致（卡方=135.199，自由度=2，p<0.0001）。硬标签BERT模型（模型A）和软标签模型（模型B）在一致帖子上的准确率（约80%）与分歧帖子上的准确率（约58%）均下降22个百分点，经p<0.0001确认。每个标注者的多头模型（模型C）进一步将差距扩大至28个百分点，同时将冒犯性分歧准确率降至0.245。关键的是，模型A在边界案例错误上的置信度显著高于模型C（0.710 vs. 0.495，p<0.0001），这意味着标准评估指标无法检测到失败。三种日益复杂的下游干预均未能恢复边界准确率。我们认为问题是结构性的。多数投票将争议性判断呈现为真实标签，模型继承了这种虚假确定性。干预必须在上游的标注设计中进行。
英文摘要：
Hate speech annotation pipelines routinely collapse annotator disagreement into majority vote labels before training. We show that this aggregation is not neutral: 42.6% of all annotator disagreement in HateXplain concentrates specifically at the hate/offensive boundary, a pattern consistent with annotators applying different thresholds for where hate begins (chi-squared = 135.199, df = 2, p < 0.0001). Both a hard-label BERT model (Model A) and a soft-label model (Model B) drop 22 percentage points in accuracy from agreed posts (~80%) to disagreement posts (~58%), confirmed at p < 0.0001. A per-annotator multi-head model (Model C) widens this gap further to 28 points while collapsing offensive disagreement accuracy to 0.245. Critically, Model A expresses significantly higher confidence on boundary case errors than Model C (0.710 vs. 0.495, p < 0.0001), meaning standard evaluation metrics will not detect the failure. Three downstream interventions of increasing sophistication all fail to recover boundary accuracy. We argue the problem is structural. Majority vote presents a contested judgment as ground truth, and models inherit that false certainty. The intervention must be upstream in annotation design.
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