Text embedding is becoming an increasingly popular AI methodology, especially
among businesses, yet the potential of text embedding models to be biased is
not well understood. This paper examines the degree to which a selection of
popular text embedding models are biased, particularly along gendered
dimensions. More specifically, this paper studies the degree to which these
models associate a list of given professions with gendered terms. The analysis
reveals that text embedding models are prone to gendered biases but in varying
ways. Although there are certain inter-model commonalities, for instance,
greater association of professions like nurse, homemaker, and socialite with
female identifiers, and greater association of professions like CEO, manager,
and boss with male identifiers, not all models make the same gendered
associations for each occupation. Furthermore, the magnitude and directionality
of bias can also vary on a model-by-model basis and depend on the particular
words models are prompted with. This paper demonstrates that gender bias
afflicts text embedding models and suggests that businesses using this
technology need to be mindful of the specific dimensions of this problem.

这篇论文研究了文本嵌入模型的偏见程度，特别是在性别维度上。分析表明，文本嵌入模型容易出现性别偏见，但表现方式各异。虽然有一些模型之间的共性，例如把护士、家庭主妇和社交女士等职业更多地与女性身份相关联，而把 CEO、经理和老板等职业更多地与男性身份相关联，但并非所有模型对每个职业都做出相同的性别关联。此外，偏见的大小和方向也会因模型和特定提示性词而有所不同。该论文证明了性别偏见对文本嵌入模型的影响，并建议使用这项技术的企业应对这一问题的特定维度保持警惕。