Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive
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| Format: | Preprint |
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2023
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| _version_ | 1866915771832074240 |
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| author | Weerasooriya, Tharindu Cyril Dutta, Sujan Ranasinghe, Tharindu Zampieri, Marcos Homan, Christopher M. KhudaBukhsh, Ashiqur R. |
| author_facet | Weerasooriya, Tharindu Cyril Dutta, Sujan Ranasinghe, Tharindu Zampieri, Marcos Homan, Christopher M. KhudaBukhsh, Ashiqur R. |
| contents | Offensive speech detection is a key component of content moderation. However, what is offensive can be highly subjective. This paper investigates how machine and human moderators disagree on what is offensive when it comes to real-world social web political discourse. We show that (1) there is extensive disagreement among the moderators (humans and machines); and (2) human and large-language-model classifiers are unable to predict how other human raters will respond, based on their political leanings. For (1), we conduct a noise audit at an unprecedented scale that combines both machine and human responses. For (2), we introduce a first-of-its-kind dataset of vicarious offense. Our noise audit reveals that moderation outcomes vary wildly across different machine moderators. Our experiments with human moderators suggest that political leanings combined with sensitive issues affect both first-person and vicarious offense. The dataset is available through https://github.com/Homan-Lab/voiced. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2301_12534 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive Weerasooriya, Tharindu Cyril Dutta, Sujan Ranasinghe, Tharindu Zampieri, Marcos Homan, Christopher M. KhudaBukhsh, Ashiqur R. Computation and Language Computers and Society Machine Learning Offensive speech detection is a key component of content moderation. However, what is offensive can be highly subjective. This paper investigates how machine and human moderators disagree on what is offensive when it comes to real-world social web political discourse. We show that (1) there is extensive disagreement among the moderators (humans and machines); and (2) human and large-language-model classifiers are unable to predict how other human raters will respond, based on their political leanings. For (1), we conduct a noise audit at an unprecedented scale that combines both machine and human responses. For (2), we introduce a first-of-its-kind dataset of vicarious offense. Our noise audit reveals that moderation outcomes vary wildly across different machine moderators. Our experiments with human moderators suggest that political leanings combined with sensitive issues affect both first-person and vicarious offense. The dataset is available through https://github.com/Homan-Lab/voiced. |
| title | Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive |
| topic | Computation and Language Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2301.12534 |