On The Fairness Impacts of Hardware Selection in Machine Learning
Sree Harsha Nelaturu Nishaanth Kanna Ravichandran Cuong Tran Sara Hooker Ferdinando Fioretto
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.