This paper proposes the use of causal modeling to detect and mitigate algorithmic bias that is nonlinear in the protected attribute. We provide a general overview of our approach. We use the German Credit data set, 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 age bias and the problem of binary classification. We show that the probability of getting correctly classified as "low risk" is lowest among young people. The probability increases with age nonlinearly. To incorporate the nonlinearity into the causal model, we introduce a higher order polynomial term. Based on the fitted causal model, the de-biased probability estimates are computed, showing improved fairness with little impact on overall classification accuracy. Causal modeling is intuitive and, hence, its use can enhance explicability and promotes trust among different stakeholders of AI.

翻译：本文提出利用因果建模检测并缓解以保护属性为自变量的非线性算法偏见。我们概述了该方法的基本框架。基于从加州大学欧文分校机器学习库下载的德国信用数据集，我们构建了：(1)作为黑箱的预测模型；(2)用于偏见缓解的因果模型。本研究聚焦于年龄偏见与二分类问题，发现年轻群体被正确分类为"低风险"的概率最低，且该概率随年龄呈非线性递增。为在因果模型中融入这种非线性特征，我们引入了高阶多项式项。基于拟合后的因果模型计算去偏概率估计值，结果表明该方法在提升公平性的同时，对整体分类精度影响极小。因果建模具有直观性，其应用能增强可解释性，促进人工智能领域各利益相关方的信任。