PDF(Pubmed)
Abstract：
In data collection for predictive modeling, underrepresentation of certain groups, based on gender, race/ethnicity, or age, may yield less accurate predictions for these groups. Recently, this issue of fairness in predictions has attracted significant attention, as data-driven models are increasingly utilized to perform crucial decision-making tasks. Existing methods to achieve fairness in the machine learning literature typically build a single prediction model in a manner that encourages fair prediction performance for all groups. These approaches have two major limitations: (i) fairness is often achieved by compromising accuracy for some groups; (ii) the underlying relationship between dependent and independent variables may not be the same across groups. We propose a joint fairness model (JFM) approach for logistic regression models for binary outcomes that estimates group-specific classifiers using a joint modeling objective function that incorporates fairness criteria for prediction. We introduce an accelerated smoothing proximal gradient algorithm to solve the convex objective function, and present the key asymptotic properties of the JFM estimates. Through simulations, we demonstrate the efficacy of the JFM in achieving good prediction performance and across-group parity, in comparison with the single fairness model, group-separate model, and group-ignorant model, especially when the minority group\'s sample size is small. Finally, we demonstrate the utility of the JFM method in a real-world example to obtain fair risk predictions for underrepresented older patients diagnosed with coronavirus disease 2019 (COVID-19).
摘要：
在用于预测建模的数据收集中，某些群体代表性不足，基于性别，种族/民族,或年龄，可能会对这些群体产生不太准确的预测。最近,预测的公平性问题引起了极大的关注，随着数据驱动的模型越来越多地用于执行关键的决策任务。在机器学习文献中实现公平性的现有方法通常以鼓励所有组的公平预测性能的方式构建单个预测模型。这些方法有两个主要限制：（i）公平性通常是通过损害某些组的准确性来实现的；（ii）不同组之间的因变量和自变量之间的基本关系可能不相同。我们提出了一种用于二元结果的逻辑回归模型的联合公平性模型（JFM）方法，该方法使用联合建模目标函数来估计特定于组的分类器，该目标函数结合了用于预测的公平性标准。我们引入了一种加速平滑近端梯度算法来求解凸目标函数，并给出了JFM估计的关键渐近性质。通过模拟,我们证明了JFM在实现良好的预测性能和跨组奇偶校验方面的有效性，与单一公平模型相比，组分离模型，和团体无知模式，特别是当少数群体的样本量较小时。最后,我们在一个真实的例子中证明了JFM方法的实用性,以获得被诊断为2019年冠状病毒病(COVID-19)的代表性不足的老年患者的公平风险预测.
