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.
@article{arxiv.2504.12433,
title = {Supporting AI-Augmented Meta-Decision Making with InDecision},
author = {Chance Castañeda and Jessica Mindel and Will Page and Hayden Stec and Manqing Yu and Kenneth Holstein},
journal= {arXiv preprint arXiv:2504.12433},
year = {2025}
}