On The Fairness Impacts of Hardware Selection in Machine Learning

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.

在机器学习生态系统中，硬件选择常常仅仅被视为一种实用工具，被算法和数据所掩盖。这种忽视在像机器学习即服务平台这样的环境中尤其成问题，在这些平台上，用户往往无法控制用于模型部署的硬件。硬件的选择如何影响泛化特性呢？本文研究了硬件对模型性能和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差异，并将这些差异归因于不同人口群体间梯度流和损失曲面的变化。通过理论和实证分析，本文不仅确定了潜在因素，还提出了一种缓解硬件导致的性能不平衡的有效策略。