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.

基础模型对推进人工智能具有巨大的潜力，但这种进展可能加剧现有的风险和不平等，导致边缘化社群被落下。我们在本立场论文中讨论，针对边缘化社群的差距 - 性能、表示、隐私、鲁棒性、可解释性和安全性 - 不是孤立的关注点，而是级联差距现象的相互关联元素。我们对比传统模型与基础模型，并强调边缘化社群面临的差距可能会加剧。此外，我们强调了基础模型中级联影响的独特威胁，相互关联的差异可能引发持久的负面影响，特别是对边缘社群的人们。我们在机器学习的背景下定义了边缘化社群，并探讨了差异的多方面性质。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，以凸显复杂的技术和社会技术环境。为了应对迫在眉睫的危机，我们总结了一系列行动呼吁，以从源头上减少差距。