Correctly resolving textual mentions of people fundamentally entails making inferences about those people. Such inferences raise the risk of systemic biases in coreference resolution systems, including biases that reinforce cis-normativity and can harm binary and non-binary trans (and cis) stakeholders. To be er understand such biases, we foreground nuanced conceptualizations of gender from sociology and sociolinguistics, and investigate where in the machine learning pipeline such biases can enter a system. We inspect many existing datasets for trans-exclusionary biases, and develop two new datasets for interrogating bias in crowd annotations and in existing coreference resolution systems. Through these studies, conducted on English text, we confirm that without acknowledging and building systems that recognize the complexity of gender, we will build systems that fail for: quality of service, stereotyping, and over- or under-representation.

了解人物性别问题对人物提及的正确解析极为重要，但有风险引起共指解析系统中的系统偏见,本文从社会学和社会语言学的角度具体阐述如何建立可以认知性别复杂性的共指识别系统，并开发了两个新数据集以此来反映和检验系统偏见。通过对英文文本的研究，证实没有认可性别复杂性的系统会导致许多潜在危害。

迈向性别包容的指代消解