Around the World in Eighty Ratings? Quantifying the Salience of Geo-Cultural Values for Pluralistic Alignment

Safe global deployment of AI models requires alignment with pluralistic human values, yet in existing safety evaluation datasets the rater pools remain largely homogeneous along geo-cultural dimensions. Through a meta-analysis of existing safety datasets, we observe that the vast majority does not include any geo-cultural information, and the ones that do, lack a robust approach to collect and understand cultural differences in safety ratings. Using the Inglehart-Welzel dimensions of cross-cultural variation, we demonstrate via hierarchical linear modeling that geo-cultural values predict safety ratings significantly better than demographic factors alone ($p<0.05$ in $6$ datasets). Further, our analysis shows that several safety datasets contain at least 10\% of culturally-sensitive items, where lack of cultural representation in the rater pool would lead to a false negative in safety classification. Finally, we provide empirical evidence that fine-tuned LLMs can identify culturally sensitive items but are not reliable at emulating judgments of raters from diverse cultural backgrounds, underscoring the critical need for continuous geo-culturally stratified (pluralistic) safety evaluations.