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Award ID contains: 2125692

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  1. ; ; ; (, FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency)
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  2. ; ; ; (, 2023 ACM Conference on Fairness, Accountability, and Transparency)
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  3. ; ; ; ; (, Proceedings of The 26th International Conference on Artificial Intelligence and Statistics)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; (, Proceedings of the ACM/SIGCHI Conference on Human Factors in Computing Systems (CHI 2023))
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  5. ; ; ; ; ; (, Proceedings of the ACM on Human-Computer Interaction)
    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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  6. ; ; ; ; (, Proceedings of the 39th International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ; ; ; (, 2022 ACM Conference on Fairness, Accountability, and Transparency)
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  8. ; ; ; ; ; ; (, 2022 ACM Conference on Fairness, Accountability, and Transparency)
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  9. ; ; ; ; ; ; ; (, Designing Interactive Systems Conference)
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  10. ; ; ; ; ; ; ; ; ; (, Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems)
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