  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.
