Prediction models can improve efficiency by automating decisions such as the approval of loan applications. However, they may inherit bias against protected groups from the data they are trained on. This paper adds counterfactual (simulated) ethnic bias to real data on mortgage application decisions, and shows that this bias is replicated by a machine learning model (XGBoost) even when ethnicity is not used as a predictive variable. Next, several other de-biasing methods are compared: averaging over prohibited variables, taking the most favorable prediction over prohibited variables (a novel method), and jointly minimizing errors as well as the association between predictions and prohibited variables. De-biasing can recover some of the original decisions, but the results are sensitive to whether the bias is effected through a proxy.

翻译：预测模型可以通过自动化决策（如贷款申请审批）来提高效率。然而，它们可能从训练数据中继承对受保护群体的偏见。本文在真实的抵押贷款申请决策数据中添加了反事实（模拟）的种族偏见，并证明即使种族未被用作预测变量，机器学习模型（XGBoost）也会复制这种偏见。随后，比较了几种去偏方法：对禁止变量进行平均化处理、采用禁止变量上的最有利预测（一种新方法），以及同时最小化误差和预测与禁止变量之间的关联性。去偏处理可以恢复部分原始决策，但结果对偏见是否通过代理变量发挥作用较为敏感。