On The Fairness Impacts of Hardware Selection in Machine Learning
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2023
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909301779464192 |
|---|---|
| author | Nelaturu, Sree Harsha Ravichandran, Nishaanth Kanna Tran, Cuong Hooker, Sara Fioretto, Ferdinando |
| author_facet | Nelaturu, Sree Harsha Ravichandran, Nishaanth Kanna Tran, Cuong Hooker, Sara Fioretto, Ferdinando |
| contents | In the machine learning ecosystem, hardware selection is often regarded as a mere utility, overshadowed by the spotlight on algorithms and data. This oversight is particularly problematic in contexts like ML-as-a-service platforms, where users often lack control over the hardware used for model deployment. How does the choice of hardware impact generalization properties? 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_03886 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | On The Fairness Impacts of Hardware Selection in Machine Learning Nelaturu, Sree Harsha Ravichandran, Nishaanth Kanna Tran, Cuong Hooker, Sara Fioretto, Ferdinando Machine Learning Artificial Intelligence Computers and Society In the machine learning ecosystem, hardware selection is often regarded as a mere utility, overshadowed by the spotlight on algorithms and data. This oversight is particularly problematic in contexts like ML-as-a-service platforms, where users often lack control over the hardware used for model deployment. How does the choice of hardware impact generalization properties? 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. |
| title | On The Fairness Impacts of Hardware Selection in Machine Learning |
| topic | Machine Learning Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2312.03886 |