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:关于如何支持决策的研究常常利用机器学习来使决策自动化或协助人类决策。我们采取了另一种改进决策的方法,利用机器学习来帮助利益攸关方提出改进和使决策过程更加公平的方法。我们创建了“与AI审议”的网络工具,使人们能够创建和评价ML模型,以便审查过去决策的长处和短处,并讨论如何改进未来的决策。我们将这一工具应用于人员甄选的背景,让利益攸关方 -- -- 决策者(理工)和决策科目(学生) -- -- 使用工具来改进研究生入学决定。我们通过案例研究,展示了利益攸关方如何利用网络工具创建ML模型,作为他们用来审议组织决策做法的边界目标。我们分享了我们研究的见解,为今后关于利益攸关方参与型的AI设计和组织决策技术的研究提供信息。