Diversity and Inclusion Metrics in Subset Selection

• URL: http://arxiv.org/abs/2002.03256v1
• Date: Sun, 9 Feb 2020 00:29:40 GMT
• Title: Diversity and Inclusion Metrics in Subset Selection
• Authors: Margaret Mitchell, Dylan Baker, Nyalleng Moorosi, Emily Denton, Ben Hutchinson, Alex Hanna, Timnit Gebru, Jamie Morgenstern
• Abstract summary: ethical concept of fairness has recently been applied in machine learning (ML) settings to describe a wide range of constraints and objectives. We introduce metrics based on these concepts, which can be applied together, separately, and in tandem with additional fairness constraints. Social choice methods can additionally be leveraged to aggregate and choose preferable sets, and we detail how these may be applied.
• Score: 17.79121536725958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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