A recent explosion of research focuses on developing methods and tools for building fair predictive models. However, most of this work relies on the assumption that the training and testing data are representative of the target population on which the model will be deployed. However, real-world training data often suffer from selection bias and are not representative of the target population for many reasons, including the cost and feasibility of collecting and labeling data, historical discrimination, and individual biases. In this paper, we introduce a new framework for certifying and ensuring the fairness of predictive models trained on biased data. We take inspiration from query answering over incomplete and inconsistent databases to present and formalize the problem of consistent range approximation (CRA) of answers to queries about aggregate information for the target population. We aim to leverage background knowledge about the data collection process, biased data, and limited or no auxiliary data sources to compute a range of answers for aggregate queries over the target population that are consistent with available information. We then develop methods that use CRA of such aggregate queries to build predictive models that are certifiably fair on the target population even when no external information about that population is available during training. We evaluate our methods on real data and demonstrate improvements over state of the art. Significantly, we show that enforcing fairness using our methods can lead to predictive models that are not only fair, but more accurate on the target population.

提出了一种新的框架，用于验证在偏差数据上训练的预测模型的公平性，其利用了数据库不完整和不一致的查询方法来解决在目标人群上进行公平性查询的一致性范围逼近(CRA)问题。在有限的目标人群统计数据或者没有这些数据的情况下，该框架利用数据收集过程和偏差数据的背景知识计算公平性查询的答案范围，并构建可验证地公平的预测模型。实验结果证明了该框架的有效性，相比现有最先进的方法有大幅度提高。

公平预测模型的一致范围近似