Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities

TL;DR

This paper discusses how foundation models can exacerbate interconnected disparities against marginalized communities, emphasizing the cascading nature of these issues and proposing calls to action for mitigation.

Contribution

It highlights the interconnected and cascading disparities in foundation models affecting marginalized communities and offers a comprehensive analysis of their sources and potential mitigation strategies.

Findings

Disparities are interconnected and can cascade, worsening impacts on marginalized groups.

Foundation models pose greater risks of disparity amplification compared to traditional models.

Mitigation requires addressing disparities throughout data creation, training, and deployment processes.

Abstract

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.