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Offensive speech detection is a key component of content moderation. However,what is offensive can be highly subjective. This paper investigates how machineand human moderators disagree on what is offensive when it comes to real-worldsocial web political discourse. We show that (1) there is extensivedisagreement among the moderators (humans and machines); and (2) human andlarge-language-model classifiers are unable to predict how other human raterswill respond, based on their political leanings. For (1), we conduct a noiseaudit at an unprecedented scale that combines both machine and human responses.For (2), we introduce a first-of-its-kind dataset of vicarious offense. Ournoise audit reveals that moderation outcomes vary wildly across differentmachine moderators. Our experiments with human moderators suggest thatpolitical leanings combined with sensitive issues affect both first-person andvicarious offense. The dataset is available throughhttps://github.com/Homan-Lab/voiced.

Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive