作者: Hal Daumé , Yang Trista Cao
DOI: 10.18653/V1/2020.ACL-MAIN.418
关键词:
摘要: Correctly resolving textual mentions of people fundamentally entails making inferences about those people. Such raise the risk systemic biases in coreference resolution systems, including that can harm binary and non-binary trans cis stakeholders. To better understand such biases, we foreground nuanced conceptualizations gender from sociology sociolinguistics, develop two new datasets for interrogating bias crowd annotations existing systems. Through these studies, conducted on English text, confirm without acknowledging building systems recognize complexity gender, build lead to many potential harms.