Shaping Choices: How AI Simplification and Framing Influence Decisions

Abstract

The emergence of Artificial Intelligence (AI) tools offers new possibilities for simplifying complex information and supporting decision-making. This study investigates how AI-simplified language and framing effects influence decision-making in scenarios involving novel military operations. Using a two (positive vs. negative framing) × two (jargon vs. AI-simplified language) between-subjects design, participants will be presented with one of two military scenarios—one involving a high-value target (HVT) and the other addressing improvised explosive device (IED) deactivation. Outcome measures include perceived desirability of the scenario, compliance, trust in AI, and cognitive workload (NASA-TLX). Drawing from prior framing studies (e.g., Tversky & Kahneman, 1981; Levin et al., 1988), we hypothesize that positively framed, AI-translated scenarios will result in higher desirability ratings, increased compliance, and lower cognitive workload compared to negatively framed or jargon-heavy versions. This research aims to inform the design of AI tools that support clear communication and user-centered decision-making aids. It highlights the importance of considering human cognition and its possible use in the design of new technologies.

Keywords

AI framing effects cognitive bias governance text simplification decision-making

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