Online images reinforce engineering stereotypes

The study, published in the Journal of the Association for Information Science and Technology, found that online images of men and women in four professions – librarian, nurse, computer programmer and civil engineer – tend to represent and reinforce existing gender stereotypes depicted across the working environment.

For the study, researchers from Rutgers University, New Jersey, analysed search results for images of people in each of the four occupations on four digital media platforms: Twitter, NYTimes.com, Wikipedia and Shutterstock.

The results saw frequent gender stereotypes and biases within these platforms. Women were overrepresented as librarians and nurses and underrepresented as computer programmers and civil engineers, especially when the collection and curation of content is largely automated by an algorithm, such as on Twitter.

“Gender bias limits the ability of people to select careers that may suit them and impedes fair practices, pay equity and equality,” said Mary Chayko, a sociologist at the School of Communication and Information (RUSCI). “Understanding the prevalence and patterns of bias and stereotypes in online images is essential, and can help us challenge, and hopefully someday break, these stereotypes.”

The study found, however, that on platforms where individuals can generate and curate content more directly, such as the NYTimes.com and Shutterstock, stereotypes were more likely to be challenged. Search results of NYTimes.com, for example, produced images of female civil engineers and male nurses more often than would be expected based on figures from the US Bureau of Labor.

“More direct content curation will help counter gender stereotypes,” said Vivek Singh, an assistant professor of library and information science at RUSCI.

While women generally tend to be underrepresented in male-dominated professions on digital media platforms, Singh also noted some progress toward equity in the gendered presentation of images from 2018 to 2019. For instance, more women were shown in images for male-dominated professions on Twitter in 2019 than in 2018.

The Rutgers team said that the study could help prevent biases from being designed into digital media platforms, algorithms, and artificial intelligence software. And while humans construct algorithms, the results of the study may also help content creators and platform designers identify whether algorithm-heavy or human-heavy curation may be better suited to a certain task.