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        <title>Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language Models</title>
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        <namePart type="family">Ignat</namePart>
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    <abstract>Despite the impressive performance of current AI models reported across various tasks, performance reports often do not include evaluations of how these models perform on the specific groups that will be impacted by these technologies. Among the minority groups under-represented in AI, data from low-income households are often overlooked in data collection and model evaluation. We evaluate the performance of a state-of-the-art vision-language model (CLIP) on a geo-diverse dataset containing household images associated with different income values (DollarStreet) and show that performance inequality exists among households of different income levels. Our results indicate that performance for the poorer groups is consistently lower than the wealthier groups across various topics and countries. We highlight insights that can help mitigate these issues and propose actionable steps for economic-level inclusive AI development.</abstract>
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%0 Conference Proceedings
%T Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language Models
%A Nwatu, Joan
%A Ignat, Oana
%A Mihalcea, Rada
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F nwatu-etal-2023-bridging
%X Despite the impressive performance of current AI models reported across various tasks, performance reports often do not include evaluations of how these models perform on the specific groups that will be impacted by these technologies. Among the minority groups under-represented in AI, data from low-income households are often overlooked in data collection and model evaluation. We evaluate the performance of a state-of-the-art vision-language model (CLIP) on a geo-diverse dataset containing household images associated with different income values (DollarStreet) and show that performance inequality exists among households of different income levels. Our results indicate that performance for the poorer groups is consistently lower than the wealthier groups across various topics and countries. We highlight insights that can help mitigate these issues and propose actionable steps for economic-level inclusive AI development.
%R 10.18653/v1/2023.emnlp-main.660
%U https://aclanthology.org/2023.emnlp-main.660
%U https://doi.org/10.18653/v1/2023.emnlp-main.660
%P 10686-10702

Markdown (Informal)

[Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language Models](https://aclanthology.org/2023.emnlp-main.660) (Nwatu et al., EMNLP 2023)

• Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language Models (Nwatu et al., EMNLP 2023)

ACL

• Joan Nwatu, Oana Ignat, and Rada Mihalcea. 2023. Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 10686–10702, Singapore. Association for Computational Linguistics.