With the widespread deployment of large-scale prediction systems in high-stakes domains, e.g., face recognition, criminal justice, etc., disparity on prediction accuracy between different demographic subgroups has called for fundamental understanding on the source of such disparity and algorithmic intervention to mitigate it. In this paper, we study the accuracy disparity problem in regression. To begin with, we first propose an error decomposition theorem, which decomposes the accuracy disparity into the distance between marginal label distributions and the distance between conditional representations, to help explain why such accuracy disparity appears in practice. Motivated by this error decomposition and the general idea of distribution alignment with statistical distances, we then propose an algorithm to reduce this disparity, and analyze its game-theoretic optima of the proposed objective functions. To corroborate our theoretical findings, we also conduct experiments on five benchmark datasets. The experimental results suggest that our proposed algorithms can effectively mitigate accuracy disparity while maintaining the predictive power of the regression models.

中文翻译：

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了解和缓解回归中的精度差异

随着在诸如人脸识别，刑事司法等高风险领域中大规模部署大型预测系统，不同人口分组之间的预测准确性差异要求对这种差异的来源和算法干预有基本的了解。减轻它。在本文中，我们研究了回归中的精度差异问题。首先，我们首先提出一个误差分解定理，它将精度差异分解为边缘标签分布之间的距离和条件表示之间的距离，以帮助解释为什么这样的精度差异在实践中会出现。受此误差分解和具有统计距离的分布对齐的一般思想的推动，我们然后提出了一种算法来减少这种差异，并分析了其对目标函数的博弈论优化。为了证实我们的理论发现，我们还对五个基准数据集进行了实验。实验结果表明，我们提出的算法可以在保持回归模型的预测能力的同时，有效缓解精度差异。