Toward Cultural Alignment: Human-Centered Evaluation of Multimodal AI Stories Across Five African Communities
Year: 2026
Type: conference-paper
Abstract: In this paper, we examine how well AIgenerated multimodal stories align with the lived practices, relationships, language, values, and visual expectations of the communities they represent.We conduct a communitygrounded mixed-methods evaluation with 19 culture representatives across five African communities, combining quantitative annotations with qualitative focus group discussions.We find that cultural alignment depends not simply on recognizable cultural markers, but on how those markers fit social, linguistic, procedural, and visual context.From these evaluations, we develop a taxonomy of cultural alignment comprising five broader cultural marker categories and eight recurring mechanisms of misalignment.We additionally evaluate five multimodal LLM judges to examine whether automated evaluation can approximate communitygrounded judgments at scale.Judge reliability and score calibration vary substantially across communities, with no single judge performing consistently across all five settings.These findings motivate community-calibrated evaluation pipelines in which automated judges are validated against community judgments to determine where they can be trusted and where human review remains necessary.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7204936046