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arXiv · 2504.12433

Supporting AI-Augmented Meta-Decision Making with InDecision

Chance Castañeda·Jessica Mindel·Will Page·Hayden Stec·Manqing Yu·Kenneth Holstein
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Abstract

From school admissions to hiring and investment decisions, the first step behind many high-stakes decision-making processes is "deciding how to decide." Formulating effective criteria to guide decision-making requires an iterative process of exploration, reflection, and discovery. Yet, this process remains under-supported in practice. In this short paper, we outline an opportunity space for AI-driven tools that augment human meta-decision making. We draw upon prior literature to propose a set of design goals for future AI tools aimed at supporting human meta-decision making. We then illustrate these ideas through InDecision, a mixed-initiative tool designed to support the iterative development of decision criteria. Based on initial findings from designing and piloting InDecision with users, we discuss future directions for AI-augmented meta-decision making.

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Chance Castañeda, Jessica Mindel, Will Page, Hayden Stec, Manqing Yu, Kenneth Holstein. 2025-04-16. Supporting AI-Augmented Meta-Decision Making with InDecision. https://arxiv.org/abs/2504.12433

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