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• W3196248941 abstract "Recently, there has been an increased interest in demographically grounded bias in natural language processing (NLP) applications. Much of the recent work has focused on describing bias and providing an overview of bias in a larger context. Here, we provide a simple, actionable summary of this recent work. We outline five sources where bias can occur in NLP systems: (1) the data, (2) the annotation process, (3) the input representations, (4) the models, and finally (5) the research design (or how we conceptualize our research). We explore each of the bias sources in detail in this article, including examples and links to related work, as well as potential counter-measures." @default.
• W3196248941 created "2021-08-30" @default.
• W3196248941 creator A5000188096 @default.
• W3196248941 creator A5084505122 @default.
• W3196248941 date "2021-08-01" @default.
• W3196248941 modified "2023-10-15" @default.
• W3196248941 title "Five sources of bias in natural language processing" @default.
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• W3196248941 doi "https://doi.org/10.1111/lnc3.12432" @default.
• W3196248941 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35864931" @default.
• W3196248941 hasPublicationYear "2021" @default.
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