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Machine learning models are commonly used to detect toxicity in onlineconversations. These models are trained on datasets annotated by human raters.We explore how raters' self-described identities impact how they annotatetoxicity in online comments. We first define the concept of specialized raterpools: rater pools formed based on raters' self-described identities, ratherthan at random. We formed three such rater pools for this study--specializedrater pools of raters from the U.S. who identify as African American, LGBTQ,and those who identify as neither. Each of these rater pools annotated the sameset of comments, which contains many references to these identity groups. Wefound that rater identity is a statistically significant factor in how raterswill annotate toxicity for identity-related annotations. Using preliminarycontent analysis, we examined the comments with the most disagreement betweenrater pools and found nuanced differences in the toxicity annotations. Next, wetrained models on the annotations from each of the different rater pools, andcompared the scores of these models on comments from several test sets.Finally, we discuss how using raters that self-identify with the subjects ofcomments can create more inclusive machine learning models, and provide morenuanced ratings than those by random raters.

Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation