Table of Contents

Distorted Reality

Online media misrepresents society

Headshot of woman with shoulder-length blond hair and yellow blazer.

Featured Researcher

Solène Delecourt

Assistant Professor, Management of Organizations

By

Dylan Walsh

Photo by

Adobe Stock

A grid of portraits of more than 50 people of different ages and ethnicities.

Even though U.S. Census data shows no systematic age differences between men and women in the workforce, that’s not what you’ll see if you search Google or YouTube or query an AI model like ChatGPT.

A new study in Nature of 1.4 million images and videos, plus nine large language models, shows striking, pervasive age and gender distortion across online images and text, where women are consistently presented as younger than men in nearly 3,500 occupational and social categories. This distortion is strongest in high-status, high-earning occupations, such as CEO or astronaut. 

“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,” says Assistant Professor Solène Delecourt, who co-authored the study with colleagues from Stanford and the University of Oxford. 

Online algorithms were found to amplify this age-gender bias. In one experiment, researchers prompted ChatGPT (GPT-4o mini) to generate and evaluate nearly 40,000 resumes across 54 occupations using distinctively male and female names. ChatGPT assumed the women were younger and less experienced and rated older male applicants more highly for the same jobs. 

The study reveals a deeply inaccurate picture of our world, one fed by the online information people increasingly consult to learn about society. When applied in real life, these AI tools are likely to reshape the world even more in line with the stereotypes inherent in their training. Delecourt calls this “a culturewide, statistical distortion of reality.”

In the case of resume screening—where AI is already widely used—AI biases are directly skewing its perceptions of who is and is not qualified for a given job. 

 “To fight pervasive cultural inequalities, the first step is to recognize how stereotypes are coded into our culture, our algorithms, and our own minds,” Delecourt says.