On The Fairness Impacts of Hardware Selection in Machine Learning

Sree Harsha Nelaturu, Nishaanth Kanna Ravichandran, Cuong Tran, Sara Hooker, Ferdinando Fioretto
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:37486-37507, 2024.

Abstract

In the machine learning ecosystem, hardware selection is often regarded as a mere utility, overshadowed by the spotlight on algorithms and data. This is especially relevant in contexts like ML-as-a-service platforms, where users often lack control over the hardware used for model deployment. This paper investigates the influence of hardware on the delicate balance between model performance and fairness. We demonstrate that hardware choices can exacerbate existing disparities, attributing these discrepancies to variations in gradient flows and loss surfaces across different demographic groups. Through both theoretical and empirical analysis, the paper not only identifies the underlying factors but also proposes an effective strategy for mitigating hardware-induced performance imbalances.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-nelaturu24a, title = {On The Fairness Impacts of Hardware Selection in Machine Learning}, author = {Nelaturu, Sree Harsha and Ravichandran, Nishaanth Kanna and Tran, Cuong and Hooker, Sara and Fioretto, Ferdinando}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {37486--37507}, 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/nelaturu24a/nelaturu24a.pdf}, url = {https://proceedings.mlr.press/v235/nelaturu24a.html}, abstract = {In the machine learning ecosystem, hardware selection is often regarded as a mere utility, overshadowed by the spotlight on algorithms and data. This is especially relevant in contexts like ML-as-a-service platforms, where users often lack control over the hardware used for model deployment. This paper investigates the influence of hardware on the delicate balance between model performance and fairness. We demonstrate that hardware choices can exacerbate existing disparities, attributing these discrepancies to variations in gradient flows and loss surfaces across different demographic groups. Through both theoretical and empirical analysis, the paper not only identifies the underlying factors but also proposes an effective strategy for mitigating hardware-induced performance imbalances.} }
Endnote
%0 Conference Paper %T On The Fairness Impacts of Hardware Selection in Machine Learning %A Sree Harsha Nelaturu %A Nishaanth Kanna Ravichandran %A Cuong Tran %A Sara Hooker %A Ferdinando Fioretto %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-nelaturu24a %I PMLR %P 37486--37507 %U https://proceedings.mlr.press/v235/nelaturu24a.html %V 235 %X In the machine learning ecosystem, hardware selection is often regarded as a mere utility, overshadowed by the spotlight on algorithms and data. This is especially relevant in contexts like ML-as-a-service platforms, where users often lack control over the hardware used for model deployment. This paper investigates the influence of hardware on the delicate balance between model performance and fairness. We demonstrate that hardware choices can exacerbate existing disparities, attributing these discrepancies to variations in gradient flows and loss surfaces across different demographic groups. Through both theoretical and empirical analysis, the paper not only identifies the underlying factors but also proposes an effective strategy for mitigating hardware-induced performance imbalances.
APA
Nelaturu, S.H., Ravichandran, N.K., Tran, C., Hooker, S. & Fioretto, F.. (2024). On The Fairness Impacts of Hardware Selection in Machine Learning. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:37486-37507 Available from https://proceedings.mlr.press/v235/nelaturu24a.html.

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