How Dozens of
Languages Help
Build Gender
Stereotypes
Usage patterns shape biases
worldwide, whether in Japanese,
Persian or English
Linguists use machine-learning
techniques for mining large text
corpora to detect how the structure
of a language lends meaning to its
words. They work on the assumption
that terms that appear in close
proximity to one another may have
similar connotations: dogs turn up
near cats more often than canines
appear close to bananas.
This same method of burrowing
into texts—more formally called
the search for distributional seman-
tics—can also provide a framework
for analyzing psychological attitudes,
including gender stereotypes that
contribute to the underrepresenta -
tion of women in scientific and
technical fields. Studies in English
have shown, for example, that the
word “woman” often appears close
to “home” and “family,” whereas
“man” is frequently paired with “job”
and “money.”
The way language fosters linguistic
stereotypes intrigued Molly Lewis, a
cognitive scientist and special faculty
member at Carnegie Mellon Universi-
ty, who focuses on the subtle ways
words convey meanings. Along with
Gary Lupyan of University of Wiscon-
sin–Madison, she de cided to build onearlier work on gen der stereotypes to
explore how common these biases
are throughout the world. In a study in
August in Nature Human Behaviour,
the researchers find that such stereo -
types are deeply embedded in 25
languages. Scientific Ameri can spoke
with Lewis about the study’s findings.
[An edited transcript of the interview
follows.]How did you come up with
the idea for the study?
There’s a lot of previous work
showing that explicit statements
about gender shape people’s
stereotypes. For example, if you
tell children that boys are better at
being doctors than girls, they will
develop a negative stereotype about
female doctors. That’s called an
explicit stereotype.
But there is little work exploring
a different aspect of language
looking at this question of gender
stereotypes from the perspective of
large-scale statistical relationships
between words. This is intended to
get at whether there is information
in language that shapes stereotypes
in a more implicit way. So you might
not even be aware that you’re being
exposed to information that could
shape your gender stereotypes.
Could you describe your
main findings?
In one case, as I mentioned, we were
focusing on the large-scale statisti-
cal relationships between words. So
to make that a little more concrete:
we had a lot of text, and we trained
machine-learning models on that
text to look at whether words such
as “man” and “career” or “man” and
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