立场：破解针对边缘化社区的级联差异之谜
Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities
AI总结：
本文指出基础模型对边缘化社区的多种差异构成级联现象并带来独特威胁，通过对比传统模型分析差异来源，提出从源头缓解差异的行动呼吁。
摘要：
基础模型的兴起为推动AI带来了巨大希望，但这种进步可能放大现有风险与不平等，使边缘化社区落后。在本立场论文中，我们讨论了针对边缘化社区的差异——性能、表征、隐私、鲁棒性、可解释性和安全性——这些并非孤立问题，而是级联差异现象的相互关联要素。我们将基础模型与传统模型进行对比，突出了其对边缘化社区加剧差异的可能性。此外，我们强调了基础模型中级联影响的独特威胁，相互关联的差异会引发持久的负面后果，特别是对处于边缘的人群。我们在机器学习语境下定义了边缘化社区，并探讨了差异的多面性。我们分析了这些差异的来源，从数据创建、训练和部署过程追溯，以揭示复杂的技术与社会技术格局。为缓解这一紧迫危机，我们最后提出了一系列行动呼吁，从源头减轻差异。
英文摘要：
The rise of foundation models holds immense promise for advancing AI, but this progress may amplify existing risks and inequalities, leaving marginalized communities behind. In this position paper, we discuss that disparities towards marginalized communities - performance, representation, privacy, robustness, interpretability and safety - are not isolated concerns but rather interconnected elements of a cascading disparity phenomenon. We contrast foundation models with traditional models and highlight the potential for exacerbated disparity against marginalized communities. Moreover, we emphasize the unique threat of cascading impacts in foundation models, where interconnected disparities can trigger long-lasting negative consequences, specifically to the people on the margin. We define marginalized communities within the machine learning context and explore the multifaceted nature of disparities. We analyze the sources of these disparities, tracing them from data creation, training and deployment procedures to highlight the complex technical and socio-technical landscape. To mitigate the pressing crisis, we conclude with a set of calls to action to mitigate disparity at its source.
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。
