论文标题
公平预测建模的一致范围近似
Consistent Range Approximation for Fair Predictive Modeling
论文作者
论文摘要
本文提出了一个新的框架，以证明对偏见数据培训的预测模型的公平性。它从查询答复不完整和不一致的数据库中得出，以制定目标人群的预测模型的一致性范围近似（CRA）的问题。该框架采用了数据收集过程的背景知识，并且有偏见的数据（无论是否有针对目标人群的统计数据）来计算公平查询的一系列答案。使用CRA，该框架构建了预测模型，这些模型对目标人群进行了证实是公平的，而不管培训期间外部数据的可用性如何。该框架的功效是通过对实际数据的评估来证明的，这表明对现有最新方法的有了显着改善。
This paper proposes a novel framework for certifying the fairness of predictive models trained on biased data. It draws from query answering for incomplete and inconsistent databases to formulate the problem of consistent range approximation (CRA) of fairness queries for a predictive model on a target population. The framework employs background knowledge of the data collection process and biased data, working with or without limited statistics about the target population, to compute a range of answers for fairness queries. Using CRA, the framework builds predictive models that are certifiably fair on the target population, regardless of the availability of external data during training. The framework's efficacy is demonstrated through evaluations on real data, showing substantial improvement over existing state-of-the-art methods.
