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Research exploring how to support decision-making has often used machine learning to automate or assist human decisions. We take an alternative approach for improving decision-making, using machine learning to help stakeholders surface ways to improve and make fairer decision-making processes. We created "Deliberating with AI", a web tool that enables people to create and evaluate ML models in order to examine strengths and shortcomings of past decision-making and deliberate on how to improve future decisions. We apply this tool to a context of people selection, having stakeholders---decision makers (faculty) and decision subjects (students)---use the tool to improve graduate school admission decisions. Through our case study, we demonstrate how the stakeholders used the web tool to create ML models that they used as boundary objects to deliberate over organization decision-making practices. We share insights from our study to inform future research on stakeholder-centered participatory AI design and technology for organizational decision-making.
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Screening Simulated Systems for Optimization
Screening procedures for ranking and selection have received less attention than selection procedures, yet they serve as a cheap and powerful tool for decision making under uncertainty. Research on screening procedures has been less active in recent years, just as the advent of parallel computing has dramatically reshaped how selection procedures are designed and implemented. As a result, screening procedures used in modern practice continue to largely operate offline on fixed data. In this tutorial, we provide an overview of screening procedures with the goal of clarifying the current state of research and laying out opportunities for future development. We discuss several guarantees delivered by screening procedures and their role in different decision-making settings and investigate their impact on screening power and sampling efficiency in numerical experiments. We also study the implementation of screening procedures in parallel computing environments and how they can be combined with selection procedures.
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- Award ID(s):
- 2206972
- PAR ID:
- 10523893
- Editor(s):
- Corlu, C G; Hunter, S R; Lam, H; Onggo, B S; Shortle, J; Biller, B
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-6966-3
- Page Range / eLocation ID:
- 1 to 15
- Format(s):
- Medium: X
- Location:
- San Antonio, TX, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/WSC60868.2023.10408036
