AI and image banks: a research methodology

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.