论文标题
机器学习公平的概念：与现实世界应用程序弥合差距
Machine learning fairness notions: Bridging the gap with real-world applications
论文作者
论文摘要
公平性是保证机器学习（ML）预测系统不会区分特定个人或整个子人群（尤其是少数族裔）的重要要求。鉴于观察公平概念的固有主观性，文献中已经引入了几种公平概念。本文是一项调查，说明了通过大量示例和场景之间的公平概念之间的微妙之处。此外，与文献中的其他调查不同，它解决了以下问题：哪种公平概念最适合给定的现实世界情景，为什么？我们试图回答这个问题的尝试在于（1）确定当前现实世界情景的一组与公平相关的特征，（2）分析每个公平概念的行为，然后（3）安装这两个元素以建议在每个特定设置中建议最合适的公平性概念。结果总结在决策图中可以由从业者和决策者使用，以导航相对较大的ML目录。
Fairness emerged as an important requirement to guarantee that Machine Learning (ML) predictive systems do not discriminate against specific individuals or entire sub-populations, in particular, minorities. Given the inherent subjectivity of viewing the concept of fairness, several notions of fairness have been introduced in the literature. This paper is a survey that illustrates the subtleties between fairness notions through a large number of examples and scenarios. In addition, unlike other surveys in the literature, it addresses the question of: which notion of fairness is most suited to a given real-world scenario and why? Our attempt to answer this question consists in (1) identifying the set of fairness-related characteristics of the real-world scenario at hand, (2) analyzing the behavior of each fairness notion, and then (3) fitting these two elements to recommend the most suitable fairness notion in every specific setup. The results are summarized in a decision diagram that can be used by practitioners and policymakers to navigate the relatively large catalog of ML.
