A joint fairness model with applications to risk predictions for underrepresented populations.
A joint fairness model with applications to risk predictions for underrepresented populations.
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DOI：
10.1111/biom.13632
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发表时间：
2023-06
期刊：
影响因子：
1.9
通讯作者：
Zhong, Judy
中科院分区：
文献类型：
--
作者：
Do, Hyungrok;Nandi, Shinjini;Putzel, Preston;Smyth, Padhraic;Zhong, Judy
在用于预测建模的数据收集中，基于性别、种族/民族或年龄的某些群体的代表性不足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人们的极大关注，因为数据驱动模型越来越多地用于执行关键的决策任务。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的局限性：i）公平性往往是通过牺牲某些群体的准确性来实现的; ii）因变量和自变量之间的潜在关系在各个群体中可能不一样。我们提出了一种联合公平模型（JFM）的方法，用于二进制结果的逻辑回归模型，该方法使用联合建模目标函数来估计特定于组的分类器，该目标函数包含预测的公平性标准。我们引入了一个加速平滑邻近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。通过仿真，我们证明了JFM在实现良好的预测性能和跨组奇偶校验，在与单一的公平模型，组分离模型，和组无知模型相比，特别是当少数群体的样本量很小的功效。最后，我们在一个真实世界的例子中展示了JFM方法的实用性，以获得被诊断患有2019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。
In data collection for predictive modeling, under-representation 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 under-represented older patients diagnosed with coronavirus disease 2019 (COVID-19).
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DOI：
10.1056/nejmp1714229
发表时间：
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期刊：
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2021-01
期刊：
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影响因子：
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