Text-to-Image (T2I) models such as DALL-E and Midjourney are gaining popularity across various global communities, where they are used to generate visual representations of distinct cultural identities. Evidence suggests that, when prompted without a specific cultural identity, the outputs of T2I models frequently reflect Western cultural norms and perspectives. Moreover, while recent research has focused on the biases and stereotypes these models can reinforce (e.g., misrepresentation, erasure, stereotyping), there has been less attention on how well these models capture the full diversity and complexity of different cultures. In this project, we are interested in examining missing perspectives regarding how culture is represented and evaluated in AI-generated images. 

Read our pre-print: A mixed-methods and community-driven evaluation of cultural sensitivity in T2I models.

Read our Response to call for input for EMRTD Study “Artificial Intelligence, Cultural Rights, and the Right to Development” issued by Expert Mechanism on the Right to Development United Nations Human Rights Office of the High Commissioner (OHCHR).

Our work informs an interactive activity that will feature in the AI+Us festival on 31st January 2026 at the University of Nottingham!