论文标题
子集选择中的多样性和包容性指标
Diversity and Inclusion Metrics in Subset Selection
论文作者
论文摘要
公平性的道德概念最近已用于机器学习（ML）设置，以描述广泛的约束和目标。当考虑道德概念与子集选择问题的相关性时，多样性和包容性的概念也适用，以创建用于社会权力和访问差异的输出。我们介绍了基于这些概念的指标，这些概念可以分别应用，并与其他公平限制一起应用。人类主题实验的结果为提出的标准提供了支持。社会选择方法还可以利用以汇总并选择可取的集合，我们详细介绍了如何应用这些集合。
The ethical concept of fairness has recently been applied in machine learning (ML) settings to describe a wide range of constraints and objectives. When considering the relevance of ethical concepts to subset selection problems, the concepts of diversity and inclusion are additionally applicable in order to create outputs that account for social power and access differentials. We introduce metrics based on these concepts, which can be applied together, separately, and in tandem with additional fairness constraints. Results from human subject experiments lend support to the proposed criteria. Social choice methods can additionally be leveraged to aggregate and choose preferable sets, and we detail how these may be applied.
