论文标题
通过特定路径特定因果效应约束学习单独的公平分类器
Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint
论文作者
论文摘要
机器学习用于为各个领域的个人做出决策，这要求我们实现良好的预测准确性，同时确保对敏感特征（例如种族和性别）的公平性。但是，在复杂的现实世界情景中，这个问题仍然很困难。为了量化这种情况下的不公平性，现有方法利用{\ it路径特异性因果效应}。但是，没有一个可以确保每个人的公平性，而无需对数据进行不切实际的功能假设。在本文中，我们提出了一个更实用的框架，以学习一个单独的公平分类器。为了避免限制性的功能假设，我们定义了单个不公平}（piu）的{\ IT概率，并解决了一个优化问题，在该问题中，可以从数据中估算的PIU上限被控制为接近零。我们阐明为什么我们的方法可以保证每个人的公平性。实验结果表明，我们的方法可以以略有准确的成本学习单独的公平分类器。
Machine learning is used to make decisions for individuals in various fields, which require us to achieve good prediction accuracy while ensuring fairness with respect to sensitive features (e.g., race and gender). This problem, however, remains difficult in complex real-world scenarios. To quantify unfairness under such situations, existing methods utilize {\it path-specific causal effects}. However, none of them can ensure fairness for each individual without making impractical functional assumptions on the data. In this paper, we propose a far more practical framework for learning an individually fair classifier. To avoid restrictive functional assumptions, we define the {\it probability of individual unfairness} (PIU) and solve an optimization problem where PIU's upper bound, which can be estimated from data, is controlled to be close to zero. We elucidate why our method can guarantee fairness for each individual. Experimental results show that our method can learn an individually fair classifier at a slight cost of accuracy.
