论文标题
通过刻板印象内容模型，社交群 - 不可思议的单词嵌入偏见
Social-Group-Agnostic Word Embedding Debiasing via the Stereotype Content Model
论文作者
论文摘要
现有的单词嵌入伪造方法需要每个社会属性（例如性别）（例如性别），这需要社交群体特定的单词对（例如“男人” - “女人”），这不能用于减轻其他社会群体的偏见，从而使这些方法不切实际或昂贵，以使这些方法不切实际或成本融合到demiassing中。我们建议，刻板印象内容模型（SCM）是一种在社会心理学中开发的理论框架，用于理解刻板印象的内容，构成刻板印象的内容沿两个心理学维度（“温暖”和“能力”构成刻板印象的内容）可以帮助借助偏见通过偏见与偏见和刻板序列之间的基础连接来捕获社会群体敏捷的努力。仅使用一对温暖的术语（例如，“真实” - “假”）和能力（例如，“智能” - “愚蠢”），我们以既定的方法进行了辩护，并发现基于性别，种族和年龄，基于SCM的偏见与特定于小组的DEMIAS相比表现
Existing word embedding debiasing methods require social-group-specific word pairs (e.g., "man"-"woman") for each social attribute (e.g., gender), which cannot be used to mitigate bias for other social groups, making these methods impractical or costly to incorporate understudied social groups in debiasing. We propose that the Stereotype Content Model (SCM), a theoretical framework developed in social psychology for understanding the content of stereotypes, which structures stereotype content along two psychological dimensions - "warmth" and "competence" - can help debiasing efforts to become social-group-agnostic by capturing the underlying connection between bias and stereotypes. Using only pairs of terms for warmth (e.g., "genuine"-"fake") and competence (e.g.,"smart"-"stupid"), we perform debiasing with established methods and find that, across gender, race, and age, SCM-based debiasing performs comparably to group-specific debiasing
