Designed to exclude? Investigating visual ageism and sexism in text-to-image AI generative models

In ongoing debate on the biases embedded in generative artificial intelligence (GenAI) systems, this paper illustrates the results of qualitative research unveiling the sexist and ageist stereotypes conveyed through images generated by ChatGPT-4o. The theoretical framework draws upon previous studies on digital ageism and sexism, focusing on visualities as carriers of socio-cultural repertoires. Accordingly, we approach GenAI as socio-technical systems and communicative agents, having a human cultural matrix. On a methodological level, we experimented with an incipient research protocol structured around three conversational threads, allowing for the contestual analysis of visual outputs across different interaction scenarios in controlled settings. The prompts refer to daily activities that involve the use of different technologies. Preliminary findings from the critical visual analysis reveal an overrepresentation of male and young people and, conversely, an invisibilization of marginalized social groups, such as female (and) older people and gender non-conforming individuals.