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Online images amplify gender bias

Social Work

Online images amplify gender bias

D. Guilbeault, S. Delecourt, et al.

This study conducted by Douglas Guilbeault, Solène Delecourt, Tasker Hull, Bhargav Srinivasa Desikan, Mark Chu, and Ethan Nadler examines how online images amplify gender bias, revealing that bias is more prominent in visuals than text. The research sheds light on the pressing need to tackle the societal implications of this shift to visual communication for a fair and inclusive internet.

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Playback language: English
Abstract
This study investigates the amplification of gender bias by online images. Analyzing millions of images and billions of words from Google, Wikipedia, and IMDb, researchers found that gender bias is significantly more prevalent in images than in text. A nationally representative experiment further demonstrated that searching for images of occupations, rather than text, amplifies gender bias in participants' beliefs. The study highlights the crucial need to address the societal impact of this shift towards visual communication to create a fair and inclusive internet.
Publisher
Nature
Published On
Feb 29, 2024
Authors
Douglas Guilbeault, Solène Delecourt, Tasker Hull, Bhargav Srinivasa Desikan, Mark Chu, Ethan Nadler
Tags
gender bias
online images
societal impact
visual communication
inclusive internet
occupational beliefs
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