Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs

• URL: http://arxiv.org/abs/2103.06076v1
• Date: Wed, 10 Mar 2021 14:26:14 GMT
• Title: Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs
• Authors: Solon Barocas, Anhong Guo, Ece Kamar, Jacquelyn Krones, Meredith Ringel Morris, Jennifer Wortman Vaughan, Duncan Wadsworth, Hanna Wallach
• Abstract summary: We argue that a deeper understanding of the choices, considerations, and tradeoffs involved in designing disaggregated evaluations will better enable researchers, practitioners, and the public to understand the ways in which AI systems may be underperforming for particular groups of people.
• Score: 42.401239658653914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Several pieces of work have uncovered performance disparities by conducting "disaggregated evaluations" of AI systems. We build on these efforts by focusing on the choices that must be made when designing a disaggregated evaluation, as well as some of the key considerations that underlie these design choices and the tradeoffs between these considerations. We argue that a deeper understanding of the choices, considerations, and tradeoffs involved in designing disaggregated evaluations will better enable researchers, practitioners, and the public to understand the ways in which AI systems may be underperforming for particular groups of people.

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