论文标题
从数据中学习个人公平指标的两种简单方法
Two Simple Ways to Learn Individual Fairness Metrics from Data
论文作者
论文摘要
个人公平是算法公平性的直观定义，它解决了群体公平的一些缺点。尽管有好处，但它取决于一个特定任务的公平指标，该指标编码了我们对当前ML任务的公平和不公平的直觉，并且缺乏广泛接受的许多ML任务公平指标，这是更广泛采用个人公平的主要障碍。在本文中，我们提供了两种简单的方法，可以从各种数据类型中学习公平指标。我们从经验上表明，对学识渊博的指标进行公平的培训会导致对三个容易受到性别和种族偏见的机器学习任务的公平性。我们还提供了两种方法的统计性能的理论保证。
Individual fairness is an intuitive definition of algorithmic fairness that addresses some of the drawbacks of group fairness. Despite its benefits, it depends on a task specific fair metric that encodes our intuition of what is fair and unfair for the ML task at hand, and the lack of a widely accepted fair metric for many ML tasks is the main barrier to broader adoption of individual fairness. In this paper, we present two simple ways to learn fair metrics from a variety of data types. We show empirically that fair training with the learned metrics leads to improved fairness on three machine learning tasks susceptible to gender and racial biases. We also provide theoretical guarantees on the statistical performance of both approaches.
