A team of psychology and linguistic researchers revealed that languages have a “male-tilt” in otherwise “gender-neutral” words and notions like the concept of a “person” or “people”. The study is now published in the journal Science Advances.
Using an artificial intelligence algorithm, the researchers studied a language repository collected by Common Crawl, a non-profit organization. This repository covered more than 630 billion mostly English–language words used by individuals in over 3 billion web pages. They considered word-meaning related to word use, linguistic context, and popular concepts. Specifically, they compared the differences and similarities in words used for gender-neutral purposes (like “individual”, “persons”, “people”, etc.), men (like “he” and “male”), and women (like “she” and “female”).
The team at New York University found that most languages have a male-tilt in their vocabularies, even in their gender-neutral terms. They inferred that, when people speak, most of the terms are directed to mean “men” and not “women”. Backed by a huge statistical margin, this tendency was observed across the use, contexts, and concepts of words.
For instance, the collective concept of a “person” or “people” overlapped more with the “male” concept than the “female” concept. Certain gender-neutral trait words like “superstitious”, “extroverted”, and “analytical” were, in fact, not gender-neutral and had their own “male” and “female” associations. Even verbs and actions—such as “smile”, “threaten”, “facilitate”, etc.—reflected this gendered bias.
The results have left researchers apprehensive about the long-term consequences of such gendered bias in something as fundamental as the choice of words. This included negative impacts on gender equality and societal decisions in fields like policy-making.
One of the lead researchers, Andrei Cimpian, observed, “Because men and women are each about half of the species, prioritizing men in our collective idea of a ‘person’ creates inequity for women in decisions based on this idea.”
To Know More You May Refer To
Bailey, A. H., Williams, A., & Cimpian, A. (2022). Based on billions of words on the internet, people = men. Science advances, 8(13), eabm2463. https://doi.org/10.1126/sciadv.abm2463