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• W3098075008 abstract "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." @default.
• W3098075008 created "2020-11-23" @default.
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• W3098075008 date "2020-02-04" @default.
• W3098075008 modified "2023-10-13" @default.
• W3098075008 title "Diversity and Inclusion Metrics in Subset Selection" @default.
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• W3098075008 doi "https://doi.org/10.1145/3375627.3375832" @default.
• W3098075008 hasPublicationYear "2020" @default.
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