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- W4205346110 abstract "The advent of social media has increased digital content—and with it, hate speech. Advancements in machine learning help detect online hate speech at scale, but scale is only one part of the problem related to moderating digital hate speech. Machines do not decide what comprises hate speech, which is part of a societal norm. Power relations establish such norms and, thus, determine who can say what comprises hate speech. Without paying attention to this data-generation process, building a fair automated hate speech detection system is unachievable. This article first examines the relationship between power, hate speech, and machine learning, then examines how the intersectional lens, focusing on power dynamics between and within social groups, helps identify bias in the data sets used to build automated hate speech detection systems." @default.
- W4205346110 created "2022-01-25" @default.
- W4205346110 creator A5011293111 @default.
- W4205346110 date "2021-04-10" @default.
- W4205346110 modified "2023-10-14" @default.
- W4205346110 title "Machines Do Not Decide Hate Speech: Machine Learning, Power, and the Intersectional Approach" @default.
- W4205346110 doi "https://doi.org/10.31235/osf.io/chvgp" @default.
- W4205346110 hasPublicationYear "2021" @default.
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