dc.contributor.author | Ulloa, Roberto | de |
dc.contributor.author | Richter, Ana Carolina | de |
dc.contributor.author | Makhortykh, Mykola | de |
dc.contributor.author | Urman, Aleksandra | de |
dc.contributor.author | Kacperski, Celina Sylwia | de |
dc.date.accessioned | 2022-09-20T10:59:08Z | |
dc.date.available | 2022-09-20T10:59:08Z | |
dc.date.issued | 2022 | de |
dc.identifier.issn | 1461-7315 | de |
dc.identifier.uri | https://www.ssoar.info/ssoar/handle/document/81399 | |
dc.description.abstract | 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. | de |
dc.language | en | de |
dc.subject.ddc | Sozialwissenschaften, Soziologie | de |
dc.subject.ddc | Social sciences, sociology, anthropology | en |
dc.subject.ddc | News media, journalism, publishing | en |
dc.subject.ddc | Publizistische Medien, Journalismus,Verlagswesen | de |
dc.subject.other | Algorithm auditing; face-ism; gender bias; image search; search engines | de |
dc.title | Representativeness and face-ism: Gender bias in image search | de |
dc.description.review | begutachtet (peer reviewed) | de |
dc.description.review | peer reviewed | en |
dc.identifier.url | localfile:/var/local/dda-files/prod/crawlerfiles/4992dd7d99bd4a7e944878271410070e/4992dd7d99bd4a7e944878271410070e.pdf | |
dc.source.journal | New Media & Society | |
dc.source.volume | 26 | |
dc.publisher.country | GBR | de |
dc.source.issue | 6 | |
dc.subject.classoz | Interactive, electronic Media | en |
dc.subject.classoz | Frauen- und Geschlechterforschung | de |
dc.subject.classoz | interaktive, elektronische Medien | de |
dc.subject.classoz | Women's Studies, Feminist Studies, Gender Studies | en |
dc.subject.thesoz | Repräsentation | de |
dc.subject.thesoz | picture | en |
dc.subject.thesoz | Experiment | de |
dc.subject.thesoz | online service | en |
dc.subject.thesoz | Algorithmus | de |
dc.subject.thesoz | Bild | de |
dc.subject.thesoz | proportion of women | en |
dc.subject.thesoz | algorithm | en |
dc.subject.thesoz | sex ratio | en |
dc.subject.thesoz | Online-Dienst | de |
dc.subject.thesoz | representation | en |
dc.subject.thesoz | search engine | en |
dc.subject.thesoz | Frauenanteil | de |
dc.subject.thesoz | Suchmaschine | de |
dc.subject.thesoz | experiment | en |
dc.subject.thesoz | Geschlechterverteilung | de |
dc.identifier.urn | urn:nbn:de:0168-ssoar-81399-6 | |
dc.rights.licence | Creative Commons - Attribution 4.0 | en |
dc.rights.licence | Creative Commons - Namensnennung 4.0 | de |
ssoar.contributor.institution | GESIS | de |
internal.status | formal und inhaltlich fertig erschlossen | de |
internal.identifier.thesoz | 10035039 | |
internal.identifier.thesoz | 10056648 | |
internal.identifier.thesoz | 10039134 | |
internal.identifier.thesoz | 10043015 | |
internal.identifier.thesoz | 10068114 | |
internal.identifier.thesoz | 10094082 | |
internal.identifier.thesoz | 10039295 | |
internal.identifier.thesoz | 10064826 | |
dc.type.stock | article | de |
dc.type.document | journal article | en |
dc.type.document | Zeitschriftenartikel | de |
dc.source.pageinfo | 3541-3567 | de |
internal.identifier.classoz | 20200 | |
internal.identifier.classoz | 1080404 | |
internal.identifier.journal | 1644 | |
internal.identifier.document | 32 | |
internal.identifier.ddc | 070 | |
internal.identifier.ddc | 300 | |
dc.identifier.doi | https://doi.org/10.1177/14614448221100699 | de |
dc.description.pubstatus | Published Version | en |
dc.description.pubstatus | Veröffentlichungsversion | de |
internal.identifier.licence | 16 | |
internal.identifier.pubstatus | 1 | |
internal.identifier.review | 1 | |
dc.subject.classhort | 20200 | de |
dc.subject.classhort | 10800 | de |
ssoar.wgl.collection | true | de |
internal.pdf.wellformed | true | |
internal.pdf.encrypted | false | |
internal.dda.reference | crawler-deepgreen-379@@4992dd7d99bd4a7e944878271410070e | |
ssoar.licence.fund | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 491156185 / Funded by the German Research Foundation (DFG) - Project number 491156185 | |