AI and image banks: a research methodology

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• dc.contributor.author Freixa Font, Pere
• dc.contributor.author Redondo i Arolas, Mar
• dc.contributor.author Codina, Lluís
• dc.contributor.author Lopezosa, Carlos
• dc.date.accessioned 2025-10-10T06:19:45Z
• dc.date.available 2025-10-10T06:19:45Z
• dc.date.issued 2025
• dc.description.abstract This chapter presents a methodological framework for analysing gender bias and the presence of sociocultural stereotypes in professional stock image banks, with a specific focus on the visual results returned by photographic and AI-generated platforms. The study is based on the hypothesis that neutral prompts — those lacking explicit references to gender, age, or ethnicity — should, in the absence of cultural or technical bias, yield a balanced visual representation across different social categories. Any significant deviation from such proportionality may indicate the existence of implicit biases or recurrent visual clichés. To explore this, the authors analysed images retrieved from four professional platforms — two based on conventional photography and two relying on AI image generation. A system of coded indicators was developed to classify the representations in terms of gender, age, ethnicity, functional diversity, beauty norms, and depicted actions. The methodology excluded group images and near-identical variants to ensure diversity and analytical rigour. The findings reveal that AI-based platforms more consistently align with user prompts (60.36%) compared to traditional photographic databases (44.84%). However, both types of platforms exhibit stereotypical patterns, suggesting a persistence of visual tropes and clichés. The proposed methodology proves effective in detecting these biases and offers a transferable analytical framework. The chapter aims to contribute to broader efforts towards more inclusive visual cultures, encouraging further interdisciplinary research on algorithmic image generation and representation in digital media.
• dc.description.sponsorship This work is part of the Project “Parameters and strategies to increase the relevance of media and digital communication in society: curation, visualisation and visibility (CUVICOM)”. Grant PID2021-123579OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF, EU.
• dc.format.mimetype application/pdf
• dc.identifier.citation Freixa P, Redondo-Arolas M, Codina L, Lopezosa C. AI and image banks: a research methodology. In: Guallar J, Vállez M, Ventura-Cisquella A, coordinators. Digital communication: trends and good practices. Barcelona: Ediciones Profesionales de la Información; 2025. p. 148-60. DOI: 10.3145/cuvicom.11.eng
• dc.identifier.uri http://hdl.handle.net/10230/71468
• dc.language.iso eng
• dc.publisher Ediciones Profesionales de la Información
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2021-123579OB-I00
• dc.rights Work distributed under a licenseCC BY-NC-SA 4.0
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/4.0/
• dc.subject.keyword Gender bias
• dc.subject.keyword Stereotypes
• dc.subject.keyword Stock image platforms
• dc.subject.keyword Artificial intelligence
• dc.subject.keyword Visual representation
• dc.subject.keyword Image prompts
• dc.subject.keyword Algorithmic interpretation
• dc.subject.keyword Iconographic analysis
• dc.subject.keyword Media representation
• dc.title AI and image banks: a research methodology
• dc.type info:eu-repo/semantics/bookPart
• dc.type.version info:eu-repo/semantics/publishedVersion