VET: A Framework for Analyzing AI Discourse
2026-06-01 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors explain that talks about AI often fall into extreme views, which can make it hard for people to learn accurately about AI. They created the VET Framework, a tool to sort AI discussions based on feelings about AI, how effective AI is seen, and what people think will happen with AI in the future. Using this framework, they look at four common types of AI conversations—overly positive, overly negative, dismissive, and balanced. Their work helps people better understand and judge these different opinions to improve AI literacy.
AI LiteracyVET FrameworkValenceEffectivenessTrajectoryAI HypeAI DoomAI DenialPublic Discourse
Authors
Meredith Ringel Morris
Abstract
Public discourse on AI has become polarized; exaggerated positions on AI in traditional and social media threaten the development of AI Literacy among the general public. In this article, I introduce the VET Framework, a method for categorizing AI discourse along the dimensions of valence, effectiveness, and trajectory. I show how this framework can be used to identify, compare, and critique prevalent narratives of AI Hype, AI Doom, AI Denial, and AI Normalcy. Using VET, I analyze how each of these four stances exaggerates some aspects of the current state and/or likely evolution of AI, and illustrate how the VET framework can serve as an AI Literacy tool by supporting the ``vetting'' of polarized AI discourse.