The word embedding association test (WEAT) is an important method for
measuring linguistic biases against social groups such as ethnic minorities in
large text corpora. It does so by comparing the semantic relatedness of words
prototypical of the groups (e.g., names unique to those groups) and attribute
words (e.g., 'pleasant' and 'unpleasant' words). We show that anti-black WEAT
estimates from geo-tagged social media data at the level of metropolitan
statistical areas strongly correlate with several measures of racial
animus--even when controlling for sociodemographic covariates. However, we also
show that every one of these correlations is explained by a third variable: the
frequency of Black names in the underlying corpora relative to White names.
This occurs because word embeddings tend to group positive (negative) words and
frequent (rare) words together in the estimated semantic space. As the
frequency of Black names on social media is strongly correlated with Black
Americans' prevalence in the population, this results in spurious anti-Black
WEAT estimates wherever few Black Americans live. This suggests that research
using the WEAT to measure bias should consider term frequency, and also
demonstrates the potential consequences of using black-box models like word
embeddings to study human cognition and behavior.

该论文研究了使用 word embeddings 进行测量种族偏见时出现的问题，结果表明需要考虑 term frequency，同时还展示了使用 black-box 模型研究人类认知和行为潜在后果。

词嵌入的地理负偏差预测种族敌意 —— 仅通过姓名频率

Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency

Bias in Word Embeddings has been a subject of recent interest, along with
efforts for its reduction. Current approaches show promising progress towards
debiasing single bias dimensions such as gender or race. In this paper, we
present a joint multiclass debiasing approach that is capable of debiasing
multiple bias dimensions simultaneously. In that direction, we present two
approaches, HardWEAT and SoftWEAT, that aim to reduce biases by minimizing the
scores of the Word Embeddings Association Test (WEAT). We demonstrate the
viability of our methods by debiasing Word Embeddings on three classes of
biases (religion, gender and race) in three different publicly available word
embeddings and show that our concepts can both reduce or even completely
eliminate bias, while maintaining meaningful relationships between vectors in
word embeddings. Our work strengthens the foundation for more unbiased neural
representations of textual data.

本文提出了一种联合多类去偏差方法 SoftWEAT，它可以在减少多个偏差方面取得良好的结果，并通过在三个不同的公开数据集上进行实验，在三个偏差分类（宗教、性别和种族）中去偏差 Word Embeddings，显示出其可行性，同时维持着词嵌入中向量的有意义关系，从而加强了更具代表性的文本数据的基础。