A new study published in Nature reveals that women are depicted as younger than men across major online platforms and artificial intelligence systems, despite real-world data showing no significant age difference between genders in the workforce. The research analyzed 1.4 million images and videos from sources such as Google, Wikipedia, IMDb, Flickr, and YouTube, along with nine large language models trained on billions of words.
Berkeley Haas Assistant Professor Solène Delecourt, co-author of the study with Douglas Guilbeault of Stanford Graduate School of Business and Bhargav Srinivasa Desikan from the University of Oxford/Autonomy Institute, said: “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. But no one has previously been able to examine this at such scale.”
The study found that women consistently appeared younger than men across 3,495 occupational and social categories. This effect was most pronounced in high-status and high-earning jobs. Algorithms used by mainstream platforms further amplified these biases. For example, when ChatGPT generated nearly 40,000 resumes for various occupations using male and 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 men.
“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.”
The researchers used multiple methods to assess gender and age in online content. These included human judgment through crowd-sourced classification tasks and objective cross-referencing where possible. They found a strong association between youthfulness and women—regardless of whether assessment was made by people or algorithms.
This pattern extended beyond images into text analysis using large datasets from Reddit, Google News, Wikipedia, and Twitter. Words related to youth were more closely associated with women.
“One concern people might have is that images and videos are kind of unique in that people can wear makeup or apply filters, using image-specific strategies to make themselves look younger,” Delecourt noted. “That’s why we also looked at text, and we found exactly the same pattern.”
To test how these representations affect perceptions in real life, researchers conducted experiments involving about 500 participants split into two groups. Those exposed to occupation-related images online estimated lower average ages for jobs held by women compared to those who did not see such images; they also recommended younger ideal hiring ages for female-dominated occupations.
In another experiment with ChatGPT (gpt-4o-mini), resumes generated for female names showed less experience compared to male counterparts for identical roles. When evaluating resumes without explicit names provided or when generating applicants itself, ChatGPT rated older men more highly than equally qualified women.
The findings suggest a feedback loop where digital portrayals influence public perception—and vice versa—potentially reinforcing stereotypes about gender roles in society.
Guilbeault highlighted: “This is of particular concern given the internet is increasingly how we learn about the social world… 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…and generative AI.”
The project received funding from several organizations including The Fisher Center for Business Analytics; The Center for Equity, Gender & Leadership; The Barbara & Gerson Bakar Fellowship; and The University of California, Berkeley.
The full paper can be accessed here: Age and gender distortion in online media and large language models (Nature).
