论文标题
通过数据预处理学习和测试反事实公平
On Learning and Testing of Counterfactual Fairness through Data Preprocessing
论文作者
论文摘要
机器学习在现实决策中变得越来越重要，但是人们关心不当使用时可能带来的道德问题。最近的工作将机器学习公平性的讨论带入了因果框架，并详细阐述了反事实公平的概念。在本文中，我们通过数据预处理（PLAP）算法开发了公平的学习，以从偏见的培训数据中学习反事实公平的决策，并正式地将不同的数据预处理程序用于保证反事实公平。我们还表明，反事实公平等同于决策的条件独立性和给定的敏感属性给定的敏感属性，这使我们能够使用已处理的数据检测原始决策中的歧视。使用模拟数据和现实世界应用程序说明了我们算法的性能。
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 paper, 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 non-sensitive 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.
