论文标题
在机器学习中的公平性：调查，反思和观点
What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective
论文作者
论文摘要
算法公平吸引了机器学习社区越来越多的关注。文献中提出了各种定义，但是它们之间的差异和联系并未清楚地解决。在本文中，我们回顾并反思了机器学习文献中先前提出的各种公平概念，并试图与道德和政治哲学，尤其是正义理论的论点建立联系。我们还从动态的角度考虑了公平询问，并进一步考虑了当前预测和决策引起的长期影响。鉴于特征公平性的差异，我们提出了一个流程图，其中包括对数据生成过程，预测结果和诱导影响的不同类型的公平询问的隐式假设和预期结果。本文展示了匹配任务的重要性（人们希望执行哪种公平性）以及能够实现预期目的的手段（公平分析的范围是什么，什么是适当的分析计划）。
Algorithmic fairness has attracted increasing attention in the machine learning community. Various definitions are proposed in the literature, but the differences and connections among them are not clearly addressed. In this paper, we review and reflect on various fairness notions previously proposed in machine learning literature, and make an attempt to draw connections to arguments in moral and political philosophy, especially theories of justice. We also consider fairness inquiries from a dynamic perspective, and further consider the long-term impact that is induced by current prediction and decision. In light of the differences in the characterized fairness, we present a flowchart that encompasses implicit assumptions and expected outcomes of different types of fairness inquiries on the data generating process, on the predicted outcome, and on the induced impact, respectively. This paper demonstrates the importance of matching the mission (which kind of fairness one would like to enforce) and the means (which spectrum of fairness analysis is of interest, what is the appropriate analyzing scheme) to fulfill the intended purpose.
