Online images amplify gender bias
Authors: D Guilbeault, S Delecourt, T Hull, BS Desikan, M Chu
Published: 2024
Publication: Nature, 2024 - nature.com
Online images significantly amplify gender bias compared to text, with biases in visual content impacting societal beliefs about gender roles.
Methods: Analyzed 3,495 social categories using over one million images from platforms like Google, Wikipedia, and IMDb, compared visual content to billions of words from the same platforms, and conducted a preregistered national experiment to assess the psychological impact on participants' beliefs.
Key Findings: The prevalence and psychological impact of gender bias in online images compared to text, including gender associations and representation disparities.
Limitations: The study focuses primarily on English content and platforms, which may not generalize to other languages or cultural contexts.
Institution: University of California Berkeley, Institute For Public Policy Research, Columbia University, University of Southern California Los Angeles
Research Area: Gender Bias, Computational Social Science,Online Media, AI Bias
Discipline: Computational Social Science
Sample Size: 3495 participants
Citations: 72
DOI: https://doi.org/10.1038/s41586-024-07068-x