A new study published in Nature reveals that women are consistently depicted as younger than men across a wide range of online platforms and by artificial intelligence systems. The research analyzed 1.4 million images and videos, as well as nine large language models, including GPT2, to assess how gender and age are represented online.
The study’s authors—Solène Delecourt from UC Berkeley Haas School of Business, Douglas Guilbeault from Stanford Graduate School of Business, and Bhargav Srinivasa Desikan from the University of Oxford/Autonomy Institute—examined content from sources such as Google, Wikipedia, IMDb, Flickr, and YouTube. They found that across 3,495 occupational and social categories, women appeared systematically younger than men.
“This kind of age-related gender bias has been seen in other studies of specific industries, and anecdotally, such as in reports of women who are referred to as girls,” said Delecourt. “But no one has previously been able to examine this at such scale.”
The disparity was most pronounced in high-status and high-earning occupations. Algorithms further intensified the bias: when ChatGPT generated nearly 40,000 resumes for various jobs using distinctively male or female names matched for popularity and ethnicity, it assumed women were younger by an average of 1.6 years and had less work experience compared to resumes with male names. Older male applicants were rated more highly for the same positions.
“Online images show the opposite of reality. And even though the internet is wrong, when it tells us this ‘fact’ about the world, and we start believing it to be true,” Guilbeault said. “It brings us deeper into bias and error.”
To assess gender and age in images and videos collected from different platforms (with stills taken from videos), researchers used multiple methods. In some cases they hired thousands of online workers to classify gender (male, female, nonbinary) and estimate ages; in others they cross-referenced image timestamps with subjects’ birthdates for precise age calculations.
Across all methods used—human judgment, machine learning tools, or objective information—the association between women with youthfulness and men with older ages remained strong. This trend intensified for prestigious jobs or those with larger pay gaps between genders.
Researchers also analyzed billions of words across Reddit, Google News, Wikipedia, Twitter/X (now X), finding that words related to youth were much more closely tied to women than men.
“One concern people might have is that images and videos are kind of unique in that people can wear makeup or apply filters… That’s why we also looked at text, and we found exactly the same pattern,” Delecourt said.
In experiments designed to test real-world effects on perception:
– Participants who viewed occupation-related images estimated lower average ages for jobs when shown women performing them; seeing men led them to assume higher average ages.
– For female-dominated occupations participants recommended younger ideal hiring ages; for male-dominated ones they suggested older hiring ages.
– When ChatGPT was prompted to generate resumes across 54 occupations using both male/female names (matched for demographic factors), resumes for women reflected less experience due partly to presumed youth.
– When evaluating resumes ChatGPT rated older men more highly than equally qualified women.
The findings suggest a feedback loop where online portrayals influence public perceptions—and may reinforce labor market inequalities over time.
“This is of particular concern given the internet is increasingly how we learn about the social world,” Guilbeault said. “People are spending more time online… Our study shows that they are reinforcing stereotypical expectations about how the world should be.”
Delecourt added: “What was most striking to me…was how this online presentation has a much broader effect than I imagined when going into this. These misrepresentations feed directly into the real world in ways that could be widening gaps in the labor market and skewing the ways we associate gender with authority and power.”
She concluded: “Overall our study shows that age-related gender bias is a culture-wide statistical distortion of reality pervading online media through images search engines videos text search engines and generative AI.”
The research was supported by grants from several organizations including The Fisher Center for Business Analytics; The Center for Equity Gender & Leadership; The Barbara & Gerson Bakar Fellowship; The University of California Berkeley.
