On Learning and Testing of Counterfactual Fairness through Data Preprocessing
提出了FLAP算法，通过数据预处理从有偏训练数据中学习反事实公平的决策，并形式化了保证反事实公平性的预处理条件，适用于机器学习公平性研究者和从业者。
Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly. Recent work brings the discussion of machine learning fairness into the causal framework and elaborates on the concept of Counterfactual Fairness. In this article, we develop the Fair Learning through dAta Preprocessing (FLAP) algorithm to learn counterfactually fair decisions from biased training data and formalize the conditions where different data preprocessing procedures should be used to guarantee counterfactual fairness. We also show that Counterfactual Fairness is equivalent to the conditional independence of the decisions and the sensitive attributes given the processed nonsensitive attributes, which enables us to detect discrimination in the original decision using the processed data. The performance of our algorithm is illustrated using simulated data and real-world applications. Supplementary materials for this article are available online.