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.