Toxicity annotators and content moderators often default to mental shortcuts
when making decisions. This can lead to subtle toxicity being missed, and
seemingly toxic but harmless content being over-detected. We introduce BiasX, a
framework that enhances content moderation setups with free-text explanations
of statements' implied social biases, and explore its effectiveness through a
large-scale crowdsourced user study. We show that indeed, participants
substantially benefit from explanations for correctly identifying subtly
(non-)toxic content. The quality of explanations is critical: imperfect
machine-generated explanations (+2.4% on hard toxic examples) help less
compared to expert-written human explanations (+7.2%). Our results showcase the
promise of using free-text explanations to encourage more thoughtful toxicity
moderation.

引入 BiasX 框架，通过大规模的众包用户研究，探索使用自由文本解释内容中隐含的社会偏见，以有效增强内容审核设置，我们发现，参与者因正确识别微妙的（非）有毒内容而受益。解释的质量很关键，完美的机器生成解释（+2.4% 的有毒难题）帮助不如专业撰写的人类解释（+7.2%）。我们的结果展示了使用自由文本解释鼓励更加深思熟虑的毒性审核的承诺。