This study investigates the impact of online images on the proliferation of gender bias. The researchers analyze gender associations in over one million images from Google, Wikipedia, and IMDb, comparing them to textual data from the same platforms. They find that gender bias is consistently more prevalent in images than text and that the underrepresentation of women online is significantly worse in images. A nationally representative experiment demonstrates that searching for images of occupations, rather than text, amplifies gender bias in participants' beliefs. The authors conclude that the increasing reliance on visual communication online exacerbates gender bias, highlighting the need to address this societal effect.

Douglas Guilbeault, Solène Delecourt, Tasker Hull, Bhargav Srinivasa Desikan, Mark Chu, Ethan Nadler