子集选择中的多样性和包容性
Diversity and Inclusion Metrics in Subset Selection
摘要：
公平的伦理概念最近被应用于机器学习（ML）设置中，以描述广泛的约束和目标。在考虑伦理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是适用的，以便创造出能够说明社会权力和获取差异的产出。我们介绍这些概念的基础上，可以一起应用，单独，并在串联与额外的公平性约束的指标。人类受试者实验的结果支持所提出的标准。社会选择方法可以另外利用聚合和选择优选的集合，我们详细介绍了如何应用这些方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
