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.

翻译：探索如何支持决策的研究常借助机器学习来自动化或辅助人类决策。我们提出了一种改进决策的替代方法：利用机器学习帮助利益相关者发现改善决策流程并使其更公平的途径。我们构建了"与AI共同商讨"这一网络工具，使用户能够创建和评估机器学习模型，以审视过往决策的优缺点，并就如何改进未来决策展开协商。我们将该工具应用于人才选拔场景，邀请利益相关者——决策者（教师）与决策对象（学生）——使用该工具优化研究生招生决策。通过案例研究，我们展示了利益相关者如何利用该网络工具创建机器学习模型，并将其作为边界对象来协商组织决策实践。我们分享了研究中的洞见，以期为未来以利益相关者为中心的参与式AI设计及组织决策技术研究提供参考。