弥合数字鸿沟：视觉-语言模型中社会经济因素导致的性能差异
Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language Models
AI总结：
本研究评估CLIP模型在Dollar Street数据集上对不同收入水平家庭图像的性能，发现低收入群体性能显著低于高收入群体，并提出促进经济包容性AI发展的可行措施。
摘要：
尽管当前AI模型在各种任务上报告的性能令人印象深刻，但这些性能报告往往未包含对这些技术在将受影响的特定群体上表现如何的评估。在AI中代表性不足的少数群体中，来自低收入家庭的数据在数据收集和模型评估中常被忽视。我们评估了最先进的视觉-语言模型（CLIP）在一个包含与不同收入值相关的家庭图像的全球多样化数据集（Dollar Street）上的性能，并表明不同收入水平的家庭之间存在性能不平等。我们的结果表明，在各种主题和国家中，较贫困群体的性能始终低于较富裕群体。我们强调了有助于缓解这些问题的见解，并提出了促进经济层面包容性AI发展的可行步骤。代码可在 https://github.com/MichiganNLP/Bridging_the_Digital_Divide 获取。
英文摘要：
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 (Dollar Street) 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. Code is available at https://github.com/MichiganNLP/Bridging_the_Digital_Divide.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
