# Supporting AI-Augmented Meta-Decision Making with InDecision

arXiv: https://arxiv.org/abs/2504.12433 · HTML: https://mlsanity.com/paper/2504.12433/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2025.

## Built on by (most cited first, 2 of 2)

- [MoMoE: Mixture of Moderation Experts Framework for AI-Assisted Online Governance](https://mlsanity.com/paper/2505.14483/) (2025)
- [Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop](https://mlsanity.com/paper/2508.21036/) (2025)

## Abstract (arXiv)

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.