Gender-Neutral Words Like ‘People’ and ‘Person’ Are Perceived as Male, Study Suggests

Researchers found that society’s concept of “person” and similar terms prioritizes men over women

Close-up of the word "dictionary" in a dictionary
The results of the analysis—that society’s concept of “person” prioritizes men over women—suggests a “fundamental bias in our species’ collective view of itself,” write the researchers in the paper. Pixabay

The words “people” and “person” are, in theory, gender neutral, used to refer to an individual or group without implying maleness or femaleness. But new research suggests that the perceived meaning of these labels is biased toward men—in other words, writes Richard A. Lovett for Cosmos magazine, “whatever terms people may use in describing the average human, they are often mentally defaulting to ‘male.’”   

Per a study published in the journal Science Advances on April 1, researchers at New York University (NYU) analyzed text from almost three billion web pages to understand how often gender-neutral words like “individual” and “humanity” overlapped with words like “he,” “she,” “male” and “female.” They found that “person” and its synonyms were more closely associated with words for men.

The results of the analysis—that society’s concept of “person” prioritizes men over women—suggests a “fundamental bias in our species’ collective view of itself,” write the researchers in the paper.

“Many forms of bias, such as the tendency to associate ‘science’ with men more than women, have been studied in the past, but there has been much less work on how we view a ‘person,’” says lead author April Bailey, a psychologist at NYU, in a statement.

To test their hypothesis, the scientists turned to a massive web data repository called Common Crawl. They analyzed more than 630 billion English words used across 2.96 billion web pages—including blogs, discussion forums and government sites—tracked by the nonprofit repository in May 2017.

Using linguistic computational techniques, the researchers looked at how often two words appeared together to determine how similar they are. The team found that terms for “person” were used more similarly to male-related words than female-related words.

The researchers also studied terms used to describe people, like “extroverted” and “superstitious,” as well as verbs like “run,” “smile” and “threaten.” Comparing these words to terms like “he” and “she,” they again found more overall overlap with words for men.

“[A.I.] learns from us, and then we learn from it.”

When they analyzed words that are stereotypically male or female, such as “brave” and “giggle,” they found men were linked with all terms, while women were more closely associated with stereotypically female words.

Common Crawl’s vast trove of data serves an array of purposes. The study, says Bailey, could have implications for a popular application of the repository: using the data to train artificial intelligence (A.I.) tools like language translation websites and chatbots.

“[A.I.] learns from us, and then we learn from it,” Bailey tells New Scientist’s Matthew Sparkes. “And we’re kind of in this reciprocal loop, where we’re reflecting it back and forth. It’s concerning because it suggests that if I were to snap my fingers right now and magically get rid of everyone’s own individual cognitive bias to think of a person as a man more than a woman, we would still have this bias in our society because it’s embedded in A.I. tools.”

Past research has uncovered other types of gender bias—men are more closely associated with science and work, while women are linked with the humanities and family, for example—but this latest study is novel in its zoomed-out approach of a concept that is “the basis for nearly all health, safety and workplace policy-making enacted in modern societies,” according to the paper.

“This is the first to study this really general gender stereotype—the idea that men are sort of the default humans—in this quantitative computational social science way,” Molly Lewis, a psychologist at Carnegie Mellon University who was not involved in the study, tells Scientific American’s Dana Smith.

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