Implementing Anti-Discrimination Policies in Statistical Profiling Models
针对统计模型中因使用与种族、性别等禁止变量相关的代理变量（如邮政编码、信用评分）而引发的歧视问题，提出一种消除代理效应同时保持预测准确性的方法，并用工人画像与再就业服务系统数据验证其价值。
How should statistical models used for assigning prices or eligibility be implemented when there is concern about discrimination? In many settings, factors such as race, gender, and age are prohibited. However, the use of variables that correlate with these omitted characteristics (e.g., zip codes, credit scores) is often contentious. We provide a framework to address these issues and propose a method that can eliminate proxy effects while maintaining predictive accuracy relative to an approach that restricts the use of contentious variables outright. We illustrate the value of our proposed method using data from the Worker Profiling and Reemployment Services system.