Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities

Golnoosh Farnadi, Mohammad Havaei, Negar Rostamzadeh
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:13072-13085, 2024.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-farnadi24a, title = {Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities}, author = {Farnadi, Golnoosh and Havaei, Mohammad and Rostamzadeh, Negar}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {13072--13085}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/farnadi24a/farnadi24a.pdf}, url = {https://proceedings.mlr.press/v235/farnadi24a.html}, 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.} }
Endnote
%0 Conference Paper %T Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities %A Golnoosh Farnadi %A Mohammad Havaei %A Negar Rostamzadeh %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-farnadi24a %I PMLR %P 13072--13085 %U https://proceedings.mlr.press/v235/farnadi24a.html %V 235 %X 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.
APA
Farnadi, G., Havaei, M. & Rostamzadeh, N.. (2024). Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:13072-13085 Available from https://proceedings.mlr.press/v235/farnadi24a.html.

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