One class of harms is representational harms (Crawford 2017, Blodgett et al. 2020), harms caused by a system that demeans a social group, for example by perpetuating negative stereotypes about them. For example Kiritchenko and Mohammad (2018) examined the performance of 200 sentiment analysis systems on pairs of sentences that were identical except for containing either a common African American first name (like Shaniqua) or a common European American first name (like Stephanie), chosen from the Caliskan et al. (2017) study discussed in Chapter 6. They found that most systems assigned lower sentiment and more negative emotion to sentences with African American names, reflecting and perpetuating stereotypes that associate African Americans with negative emotions (Popp et al., 2003) . In other tasks classifiers may lead to both representational harms and other harms, such as censorship. For example the important text classification task of toxicity detection is the task of detecting hate speech, abuse, harassment, or other toxicity detection kinds of toxic language. While the goal of such classifiers is to help reduce societal harm, toxicity classifiers can themselves cause harms. For example, researchers have shown that some widely used toxicity classifiers incorrectly flag as being toxic sentences that are non-toxic but simply mention minority identities like women (Park et al., 2018), blind people (Hutchinson et al., 2020) or gay people (Dixon et al., 2018), or simply use linguistic features characteristic of varieties like African-American Vernacular English (Sap et al. 2019 , Davidson et al. 2019 . Such false positive errors, if employed by toxicity detection systems without human oversight, could lead to the censoring of discourse by or about these groups.