  Unequal representation across cultures and socioeconomic groups in AI is a
significant and challenging problem, often leading to uneven model performance.
As a step toward addressing this issue, we formulate translated non-English,
geographic, and socioeconomic integrated prompts and evaluate their impact on
VL model performance for data from different countries and income groups. Our
findings show that geographic and socioeconomic integrated prompts improve VL
performance on lower-income data and favor the retrieval of topic appearances
commonly found in data from low-income households. From our analyses, we
identify and highlight contexts where these strategies yield the most
improvements. Our model analysis code is publicly available at
https://github.com/Anniejoan/Uplifting-Lower-income-data .
