Every day we are bombarded with digital images. They appear on our social networks, in our search results and on the websites we browse. People send them to us through messaging apps or by email. At the end of today, billions more will have been uploaded and shared online.

With The average user spends 6 hours and 40 minutes a day on the Internet.According to a report, these images constitute an important part of our daily visual information.

And recent research indicates that they may even be influencing our perceptions.

A study published earlier this year analyzed images on Google, Wikipedia, and the Internet Movie Database (IMBD), specifically looking at which genders were predominant when searching for different occupations, such as “farmer,” “CEO,” or “TV reporter.” The findings were stark. Although women were generally underrepresented, gender stereotypes were strong. Categories like “plumber,” “developer,” “investment banker,” and “cardiac surgeon” were much more likely to be male. “Housekeeper,” “nurse practitioner,” “entertainer,” and “ballet dancer” tended to be women.

So far, nothing surprising. As an anecdote, I I encountered the same phenomenon myself in 2019, when I was trying to find gender balanced images for this website. Searching Getty Creative, one of our main stock photography sites, I discovered that photographs of male doctors outnumbered female doctors by a ratio of three to one, although in the United States, for example, Doctors under age 44 at the time were more likely to be women than men.. This description by medical professionals was only part of the problem. There were twice as many options for photos of women with babies, or even women with salads, as there were of men.

However, the latest study went a step further. Rather than simply showing the extent of gender bias in online images, the researchers tested whether exposure to these images had any impact on people’s own biases. In the experiment, 423 American participants used Google to search for different occupations. Two groups performed text searches, using Google or Google News; another group used Google Images instead. (A control group also used Google, but to search for categories unrelated to occupations, such as “apple” and “guitar.”) All participants were then administered an “implicit association test,” which measures implicit biases.

Compared to participants who searched Google for text-based descriptions of occupations, participants who used Google Images and received visual representations in response showed much higher rates of implicit gender bias after the experiment, both immediately after and three days later.