TY - GEN
AU - Ulloa, Roberto
AU - Richter, Ana Carolina
AU - Makhortykh, Mykola
AU - Urman, Aleksandra
AU - Kacperski, Celina Sylwia
TI - Representativeness and face-ism: Gender bias in image search
KW - Repräsentation
KW - Experiment
KW - Algorithmus
KW - Bild
KW - Online-Dienst
KW - Frauenanteil
KW - Suchmaschine
KW - Geschlechterverteilung
KW - Algorithm auditing
KW - face-ism
KW - gender bias
KW - image search
KW - search engines
PY - 2022
N2 - Veröffentlichungsversion
N2 - begutachtet (peer reviewed)
N2 - In: New Media & Society ; 26 (2022) 6 ; 3541-3567
N2 - Implicit and explicit gender biases in media representations of individuals have long existed. Women are less likely to be represented in gender-neutral media content (representation bias), and their face-to-body ratio in images is often lower (face-ism bias). In this article, we look at representativeness and face-ism in search engine image results. We systematically queried four search engines (Google, Bing, Baidu, Yandex) from three locations, using two browsers and in two waves, with gender-neutral (person, intelligent person) and gendered (woman, intelligent woman, man, intelligent man) terminology, accessing the top 100 image results. We employed automatic identification for the individual’s gender expression (female/male) and the calculation of the face-to-body ratio of individuals depicted. We find that, as in other forms of media, search engine images perpetuate biases to the detriment of women, confirming the existence of the representation and face-ism biases. In-depth algorithmic debiasing with a specific focus on gender bias is overdue.
BT - Interaktive, elektronische Medien
UR - http://slubdd.de/katalog?TN_libero_mab2
ER -
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