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.

翻译：在机器学习生态系统中，硬件选择往往被视为单纯的实用工具，其重要性被算法与数据的光芒所掩盖。这种忽视在机器学习即服务（ML-as-a-service）平台等场景中尤为突出，用户通常无法控制模型部署所使用的硬件。硬件选择如何影响模型的泛化特性？本文深入探究硬件对模型性能与公平性之间微妙平衡的影响。我们证明，硬件选择会加剧现有的性能差异，并将这些差异归因于不同人口群体间梯度流与损失曲面的差异。通过理论分析与实证研究相结合，本文不仅揭示了根本诱因，还提出了一种有效缓解硬件引发性能失衡的策略。