Women are systematically portrayed as younger than men across online platforms, with artificial intelligence algorithms amplifying this age-related gender bias when generating and evaluating resumes, according to research from the University of California Berkeley analysing 1.4 million images and videos.
The study, published in Nature, examined content from Google, Wikipedia, IMDb, Flickr and YouTube, plus nine large language models trained on billions of words, finding women consistently appeared younger than men across 3,495 occupational and social categories. The distortion proved most stark for high-status, high-earning occupations and for jobs with larger pay gaps between men and women.
Solène Delecourt, Assistant Professor at Berkeley Haas who co-authored the study with Douglas Guilbeault of Stanford’s Graduate School of Business and Bhargav Srinivasa Desikan from the University of Oxford and 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 researchers used multiple approaches to assess gender and age in images and videos, hiring thousands of online workers to classify gender and estimate age within set ranges, whilst other datasets allowed them to cross-reference image timestamps with subjects’ birthdates to calculate objectively precise ages. Women were strongly associated with youth and men with older ages across all methods and datasets, whether measured by human judgement, machine learning or objective information.
The same pattern emerged when the researchers shifted analysis from images to text, studying the relationship between gender and age using billions of words from across the internet including Reddit, Google News, Wikipedia and Twitter. Words related to youth proved much more closely tied to women.
The team conducted two experiments to understand how online algorithms amplify this bias. In the first, roughly 500 participants split into two groups searched either Google Images for specific occupations or unrelated images as a control, then estimated average ages and hiring preferences. Participants who viewed women in occupation-related images estimated significantly lower average ages for those jobs compared to the control group, whilst those who saw men performing the same job assumed significantly higher average ages.
In the second experiment, ChatGPT generated nearly 40,000 resumes across 54 occupations using distinctively male and female names matched for popularity, ethnicity and other factors. When generating resumes for women, ChatGPT assumed they were younger by 1.6 years, had more recent graduation dates and had less work experience compared to resumes with male names. When evaluating resumes, ChatGPT rated older men more highly than women for the same positions regardless of whether researchers provided names or the system generated its own applicants.
Guilbeault said: “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. It brings us deeper into bias and error.”
The research follows a study published in Nature last year by Delecourt and Guilbeault finding female and male gender associations are more extreme among Google Images than within text from Google News, with bias over four times stronger in images than text. The researchers also found biases are more psychologically potent in visual form.
Guilbeault noted this evaluation of online information at unprecedented scale reveals a deeply inaccurate picture of the world, with particular concern given the internet is increasingly how people learn about the social world. The researchers warned these biased beliefs are spreading and becoming a self-fulfilling prophecy that reinforces stereotypical expectations.
Delecourt said: “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.” She pointed to the amount of information young people absorb through online experience, noting children may be imprinted with biased ideas about occupations based on what images present for the average male or female doctor, for example.
US Census data shows no systematic age differences between men and women in the workforce over the past decade, whilst globally women on average live about five years longer than men. However, these facts are not reflected in online content or AI outputs.
