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arXiv · 2305.13589

BiasX: "Thinking Slow" in Toxic Content Moderation with Explanations of Implied Social Biases

Yiming Zhang·Sravani Nanduri·Liwei Jiang·Tongshuang Wu·Maarten Sap
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Abstract

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.

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Yiming Zhang, Sravani Nanduri, Liwei Jiang, Tongshuang Wu, Maarten Sap. 2023-05-23. BiasX: "Thinking Slow" in Toxic Content Moderation with Explanations of Implied Social Biases. https://arxiv.org/abs/2305.13589

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