mlsanityData to 14 Oct 2026

On The Fairness Impacts of Hardware Selection in Machine Learning

arXiv 2312.03886 ↗First posted 6 Dec 2023cs.LG cs.AI cs.CYcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 44 of its 58 references that are in the map. First posted on arXiv 6 Dec 2023.

Builds on

44 of 58 references.

References · 44

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32 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

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