This paper proposes the use of causal modeling to detect and mitigate algorithmic bias. We provide a brief description of causal modeling and a general overview of our approach. We then use the Adult dataset, which is available for download from the UC Irvine Machine Learning Repository, to develop (1) a prediction model, which is treated as a black box, and (2) a causal model for bias mitigation. In this paper, we focus on gender bias and the problem of binary classification. We show that gender bias in the prediction model is statistically significant at the 0.05 level. We demonstrate the effectiveness of the causal model in mitigating gender bias by cross-validation. Furthermore, we show that the overall classification accuracy is improved slightly. Our novel approach is intuitive, easy-to-use, and can be implemented using existing statistical software tools such as "lavaan" in R. Hence, it enhances explainability and promotes trust.

翻译：本文提出利用因果建模来检测和缓解算法偏差。我们简要描述了因果建模的基本概念及本文方法的总体框架。随后使用可从加州大学欧文分校机器学习知识库下载的Adult数据集，分别构建：(1) 视为黑箱的预测模型, (2) 用于偏差缓解的因果模型。本文聚焦于性别偏差与二分类问题，实证表明预测模型中的性别偏差在0.05置信水平上具有统计显著性。通过交叉验证，我们验证了因果模型在缓解性别偏差方面的有效性。此外，整体分类准确率略有提升。本方法直观易用，可利用现有统计软件工具（如R语言中的"lavaan"包）实现，从而增强模型可解释性并提升可信度。