Econ Papers Daily / 期刊论文
数据丰富环境下非对称损失下的二元选择：理论与算法公平性应用
Binary choice under asymmetric loss in a data‐rich environment: Theory and an application to algorithmic fairness
摘要
We study the binary choice problem in a data‐rich environment with asymmetric loss functions. The econometrics literature covers nonparametric binary choice problems but does not offer computationally attractive solutions in data‐rich environments. The machine learning literature has many algorithms but is focused mostly on loss functions that are independent of covariates. We show that theoretically valid decisions on binary outcomes with general loss functions can be achieved via a very simple loss‐based reweighting of logistic regression or state‐of‐the‐art machine learning techniques. We apply our analysis to algorithmic fairness in pretrial detentions.
中文摘要
我们研究了数据丰富环境下具有非对称损失函数的二元选择问题。计量经济学文献涵盖了非参数二元选择问题，但未提供在数据丰富环境中具有计算吸引力的解决方案。机器学习文献虽包含众多算法，但主要关注与协变量无关的损失函数。我们证明，通过基于损失函数对逻辑回归或前沿机器学习技术进行极其简单的重新加权，即可在一般损失函数下实现理论上有效的二元结果决策。我们将分析应用于审前羁押中的算法公平性问题。