论文标题
不平等表示：使用表示相似性分析分析单词嵌入中的交叉偏差
Unequal Representations: Analyzing Intersectional Biases in Word Embeddings Using Representational Similarity Analysis
论文作者
论文摘要
我们提出了一种新的方法，用于使用代表性相似性分析在单词嵌入中检测类似人类的社会偏见。具体而言，我们探究了情境化和非上下文化的嵌入，以证明针对黑人妇女的交叉偏见。我们表明，这些嵌入代表黑人妇女的女性比白人女性少，而黑人的女性则比黑人男性少。这一发现与相交理论一致，该理论认为，多个身份类别（例如种族或性别）彼此之上，以创建任何单个类别都不共享的独特歧视模式。
We present a new approach for detecting human-like social biases in word embeddings using representational similarity analysis. Specifically, we probe contextualized and non-contextualized embeddings for evidence of intersectional biases against Black women. We show that these embeddings represent Black women as simultaneously less feminine than White women, and less Black than Black men. This finding aligns with intersectionality theory, which argues that multiple identity categories (such as race or sex) layer on top of each other in order to create unique modes of discrimination that are not shared by any individual category.
