Diversity and Inclusion Metrics in Subset Selection
  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.
